From d1b3571005c3e67d2f41873813bfeec31c0f19d5 Mon Sep 17 00:00:00 2001 From: beykyle Date: Fri, 18 Sep 2026 11:51:52 -0400 Subject: [PATCH 01/11] fix bug in geometric part of p,n amplitude --- examples/notebooks/chex_jitr_validation.ipynb | 98 +++-- examples/notebooks/chex_qepn_xs.txt | 360 +++++++++--------- src/jitr/xs/quasielastic_pn.py | 4 +- tests/test_quasielastic_pn_spin_flip.py | 97 +++++ 4 files changed, 341 insertions(+), 218 deletions(-) create mode 100644 tests/test_quasielastic_pn_spin_flip.py diff --git a/examples/notebooks/chex_jitr_validation.ipynb b/examples/notebooks/chex_jitr_validation.ipynb index 28699344..24e1865f 100644 --- a/examples/notebooks/chex_jitr_validation.ipynb +++ b/examples/notebooks/chex_jitr_validation.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "81bba3a5", "metadata": { "tags": [ @@ -44,15 +44,12 @@ "source": [ "## Comparison of quasi-elastic $(p,n)$ differential cross sections between jitR and CHEX\n", "\n", - "CHEX uses non-relativistic kinematics, so for a like-for-like comparison both the\n", - "entrance and exit channels are set up here with `relativistic=False`. jitR defaults\n", - "to the semi-relativistic (Ingemarsson) prescription, which changes the forward-angle\n", - "cross section at the ~10% level at this energy." + "CHEX uses non-relativistic kinematics, wheras jitR defaults to the semi-relativistic (Ingemarsson) prescription, so for a like-for-like comparison both the entrance and exit channels are set up here with `relativistic=False. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "e6ba03f8", "metadata": {}, "outputs": [], @@ -64,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "fdd41822-23c4-4230-b119-fbced5089832", "metadata": {}, "outputs": [], @@ -74,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "38a6f920", "metadata": {}, "outputs": [], @@ -102,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "187d389a-88bf-4d76-bd1e-f9447d805c76", "metadata": {}, "outputs": [], @@ -114,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "adc61bc4-fd9f-459f-a496-424acd54d0be", "metadata": {}, "outputs": [], @@ -129,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "9c90f896-1b92-4c41-a1e7-1eab3392fdf1", "metadata": {}, "outputs": [], @@ -142,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "89e1f3fc-25cf-49e3-b7cd-a1faed13da22", "metadata": {}, "outputs": [ @@ -152,7 +149,7 @@ "ChannelKinematics(Elab=35, Ecm=34.27976496842483, mu=918.9637641236778, k=np.float64(1.2720279057856945), eta=np.float64(0.5343312852705087))" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -163,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "08d7aa13-c56e-4b0f-9862-f3621b9c6e33", "metadata": {}, "outputs": [ @@ -173,7 +170,7 @@ "ChannelKinematics(Elab=27.676442482181717, Ecm=27.106217022054977, mu=920.2073684974115, k=np.float64(1.1318942051723602), eta=np.float64(0.0))" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -184,19 +181,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "143ea4f8-9b66-4eb3-bbcf-22f8b3f1e1b4", "metadata": {}, "outputs": [], "source": [ - "channel_radius_fm = 16 # fm\n", + "channel_radius_fm = 12 # fm\n", "lmax = 20\n", "angles = np.linspace(0.01, np.pi, 180)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "af4644ae-80d6-4b4a-8afb-ea9e467ddd51", "metadata": {}, "outputs": [ @@ -204,7 +201,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "35\n" + "25\n" ] } ], @@ -216,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "dc19976d-76f7-4401-925e-dd4a8d3a6d50", "metadata": {}, "outputs": [], @@ -235,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "f3dc176e-e2de-496e-b65b-d0d29b611267", "metadata": {}, "outputs": [ @@ -245,7 +242,7 @@ "48-Ca(p,n)48-Sc" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -256,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "b8f93162-f9de-4e6a-a1a2-37d9e56d372c", "metadata": {}, "outputs": [], @@ -267,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "4b3f74b9-4797-4efb-9528-1a501b5d4832", "metadata": {}, "outputs": [], @@ -280,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "44c2b2fa-3420-4081-b3dc-6f46c1e98fe4", "metadata": {}, "outputs": [], @@ -292,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "cd68ff12-8ff4-4c20-82ca-8072885e56a9", "metadata": {}, "outputs": [], @@ -308,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "26813ce5-298e-4e6e-a767-391df2590723", "metadata": {}, "outputs": [], @@ -322,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "e07a8f8d-b8d5-48d5-a085-03dadb12fba2", "metadata": {}, "outputs": [], @@ -352,23 +349,49 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "id": "a2b503d1-5468-46bf-a884-ff5d0c8a719d", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "" + "
" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" - }, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(\n", + " ca48_pn_ias[:, 0],\n", + " ca48_pn_ias[:, 1],\n", + " yerr=ca48_pn_ias[:, 2],\n", + " label=\"Jon et al., \",\n", + " linestyle=\"none\",\n", + " marker=\".\",\n", + ")\n", + "\n", + "plt.plot(workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR\")\n", + "plt.plot(xspn[\"theta\"], xspn[\"dxs\"], label=\"CHEX\", alpha=0.5)\n", + "plt.xlabel(r\"$\\theta$ [deg]\")\n", + "plt.ylabel(r\" $d \\sigma / \\Omega$ [mb/Sr]\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "623d1c97-f9fe-42b5-bd6b-57375ad69834", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -391,12 +414,15 @@ "plt.plot(xspn[\"theta\"], xspn[\"dxs\"], label=\"CHEX\", alpha=0.5)\n", "plt.xlabel(r\"$\\theta$ [deg]\")\n", "plt.ylabel(r\" $d \\sigma / \\Omega$ [mb/Sr]\")\n", - "plt.legend()" + "plt.legend()\n", + "plt.yscale(\"log\")\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "22e9e190-1f57-41aa-b521-62e2f8d718d9", "metadata": {}, "outputs": [], diff --git a/examples/notebooks/chex_qepn_xs.txt b/examples/notebooks/chex_qepn_xs.txt index 20116bc3..ef294baf 100644 --- a/examples/notebooks/chex_qepn_xs.txt +++ b/examples/notebooks/chex_qepn_xs.txt @@ -1,180 +1,180 @@ - 0.0000000000000000 1.6752869555624581 - 1.0000000000000000 1.6804820601303603 - 2.0000000000000000 1.6960629667735683 - 3.0000000000000000 1.7220108448450666 - 4.0000000000000000 1.7582760924884138 - 5.0000000000000000 1.8047526571906038 - 6.0000000000000000 1.8612452512222577 - 7.0000000000000000 1.9274323428000764 - 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8.7147205737755792E-002 + 177.00000000000000 8.9445810953048557E-002 + 178.00000000000000 9.1127579056065153E-002 + 179.00000000000000 9.2152857826333631E-002 diff --git a/src/jitr/xs/quasielastic_pn.py b/src/jitr/xs/quasielastic_pn.py index b0308721..dd03b0f8 100644 --- a/src/jitr/xs/quasielastic_pn.py +++ b/src/jitr/xs/quasielastic_pn.py @@ -202,7 +202,7 @@ def __init__( ): if abs(m - mp) <= l and jp >= 0: ylm = sph_harm_y(l, int(m - mp), self.angles, 0) - cg0 = clebsch_gordan(l, 1 / 2, jp, m - mp, m, mp) + cg0 = clebsch_gordan(l, 1 / 2, jp, m - mp, mp, m) cg1 = clebsch_gordan(l, 1 / 2, jp, 0, m, m) self.geometric_factor[im, imp, l, ijp, :] = ( @@ -412,7 +412,7 @@ def xs( # TODO cast into a np.sum for im, m in enumerate([-0.5, 0.5]): for imp, mp in enumerate([-0.5, 0.5]): - for l in range(0, self.sys.lmax): + for l in range(0, self.sys.lmax + 1): for ijp, jp in enumerate([l + 0.5, l - 0.5]): if abs(m - mp) <= l and jp >= 0: Tmmp[im, imp, :] += ( diff --git a/tests/test_quasielastic_pn_spin_flip.py b/tests/test_quasielastic_pn_spin_flip.py new file mode 100644 index 00000000..758ba472 --- /dev/null +++ b/tests/test_quasielastic_pn_spin_flip.py @@ -0,0 +1,97 @@ +import numpy as np +import pytest + +from jitr.reactions import Reaction +from jitr.rmatrix import Solver +from jitr.xs.quasielastic_pn import Workspace + + +def _woods_saxon(rgrid: np.ndarray, depth: complex, R: float, a: float) -> np.ndarray: + return np.asarray(depth / (1 + np.exp((rgrid - R) / a)), dtype=np.complex128) + + +def _thomas(rgrid: np.ndarray, depth: complex, R: float, a: float) -> np.ndarray: + # derivative Woods-Saxon form factor used for spin-orbit coupling + x = np.exp((rgrid - R) / a) + return np.asarray(-depth * x / (1 + x) ** 2 / (a * rgrid), dtype=np.complex128) + + +@pytest.fixture(scope="module") +def workspace() -> Workspace: + reaction = Reaction((48, 20), (1, 1), (1, 0), (48, 21)) + kinematics_entrance = reaction.kinematics(35.0, relativistic=False) + kinematics_exit = reaction.kinematics_exit( + kinematics_entrance, 6.67, relativistic=False + ) + return Workspace( + reaction=reaction, + kinematics_entrance=kinematics_entrance, + kinematics_exit=kinematics_exit, + solver=Solver(35), + angles=np.linspace(0.05, np.pi - 0.05, 60), + lmax=15, + channel_radius_fm=14.0, + tmatrix_abs_tol=1e-12, + ) + + +def _potentials(workspace: Workspace) -> dict[str, np.ndarray]: + rgrid = workspace.radial_grid() + return { + "U_p_coulomb": _woods_saxon(rgrid, 8.0, 4.7, 0.3), + "U_p_central": _woods_saxon(rgrid, -50.0 - 8.0j, 4.4, 0.65), + "U_p_spin_orbit": _thomas(rgrid, 6.0 - 0.3j, 4.0, 0.6), + "U_n_central": _woods_saxon(rgrid, -46.0 - 8.0j, 4.4, 0.65), + "U_n_spin_orbit": _thomas(rgrid, 5.5 - 0.3j, 4.0, 0.6), + } + + +def _spin_amplitudes(workspace: Workspace, **potentials: np.ndarray) -> np.ndarray: + Tlj, _, _ = workspace.tmatrix(**potentials) + return np.einsum("abljt,lj->abt", workspace.geometric_factor, Tlj) + + +def test_spin_flip_geometric_factors_nonzero(workspace: Workspace) -> None: + gf = workspace.geometric_factor + for l in range(1, workspace.lmax + 1): + for ijp in range(2): + assert np.max(np.abs(gf[0, 1, l, ijp])) > 0 + assert np.max(np.abs(gf[1, 0, l, ijp])) > 0 + # s-wave cannot flip spin + np.testing.assert_array_equal(gf[0, 1, 0], 0) + np.testing.assert_array_equal(gf[1, 0, 0], 0) + + +def test_spin_flip_cancels_over_j(workspace: Workspace) -> None: + # CG orthogonality: for j-independent T_lj the spin-flip amplitude vanishes + gf = workspace.geometric_factor + scale = np.max(np.abs(gf)) + np.testing.assert_allclose(gf[0, 1].sum(axis=1), 0, atol=1e-12 * scale) + np.testing.assert_allclose(gf[1, 0].sum(axis=1), 0, atol=1e-12 * scale) + + +def test_no_spin_orbit_has_no_spin_flip(workspace: Workspace) -> None: + potentials = _potentials(workspace) + potentials.pop("U_p_spin_orbit") + potentials.pop("U_n_spin_orbit") + T = _spin_amplitudes(workspace, **potentials) + np.testing.assert_allclose(T[0, 1], 0, atol=1e-10 * np.max(np.abs(T))) + np.testing.assert_allclose(T[1, 0], 0, atol=1e-10 * np.max(np.abs(T))) + + +def test_spin_orbit_produces_spin_flip(workspace: Workspace) -> None: + potentials = _potentials(workspace) + T = _spin_amplitudes(workspace, **potentials) + xs = workspace.xs(**potentials) + + non_flip = workspace.xs_factor * 10 * (np.abs(T[0, 0]) ** 2 + np.abs(T[1, 1]) ** 2) + flip = workspace.xs_factor * 10 * (np.abs(T[0, 1]) ** 2 + np.abs(T[1, 0]) ** 2) + np.testing.assert_allclose(xs, non_flip + flip, rtol=1e-12) + + # parity: |T_{++}| = |T_{--}| and |T_{+-}| = |T_{-+}| in the scattering plane + np.testing.assert_allclose(np.abs(T[0, 0]), np.abs(T[1, 1]), rtol=1e-10) + np.testing.assert_allclose(np.abs(T[0, 1]), np.abs(T[1, 0]), rtol=1e-10) + + # spin-flip vanishes at 0 and 180 degrees but is sizable at intermediate angles + mid = (workspace.angles > np.pi / 4) & (workspace.angles < 3 * np.pi / 4) + assert np.mean(flip[mid] / xs[mid]) > 0.05 From eb510416074894118b72c49554e3df5e7370f00d Mon Sep 17 00:00:00 2001 From: beykyle Date: Fri, 18 Sep 2026 12:17:32 -0400 Subject: [PATCH 02/11] allow user-supplied U1 transition potentials in (p,n) workspace Workspace.tmatrix() and .xs() take optional U1_central and U1_spin_orbit. When None, each defaults independently to -(U_n - U_p) * isovector_factor; when given, the array is used as-is in the radial integral. Add tests for default equivalence, independent defaults, linear scaling of T with U1, and shape validation. Add a channel-energy vs midpoint-energy U1 comparison to the CHEX validation notebook (not asserted against CHEX). --- examples/notebooks/chex_jitr_validation.ipynb | 88 +++++++++++++++++-- src/jitr/xs/quasielastic_pn.py | 34 ++++++- tests/test_quasielastic_pn_spin_flip.py | 68 ++++++++++++++ 3 files changed, 178 insertions(+), 12 deletions(-) diff --git a/examples/notebooks/chex_jitr_validation.ipynb b/examples/notebooks/chex_jitr_validation.ipynb index 24e1865f..244282aa 100644 --- a/examples/notebooks/chex_jitr_validation.ipynb +++ b/examples/notebooks/chex_jitr_validation.ipynb @@ -303,9 +303,71 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "8be24130", + "metadata": {}, + "source": [ + "### Channel-energy vs. midpoint-energy transition potential\n", + "\n", + "By default, the (p,n) transition potential is built from the difference of the\n", + "entrance- and exit-channel optical potentials, each evaluated at its own channel energy:\n", + "$U_1 = -(U_n(E_n) - U_p(E_p)) \\sqrt{|N-Z|}/(N-Z-1)$.\n", + "\n", + "An alternative is to evaluate both $U_p$ and $U_n$ at the midpoint energy\n", + "$E_\\mathrm{mid} = (E_p + E_n)/2$ when constructing $U_1$. The distorting potentials\n", + "stay at their channel energies. A custom transition potential can be passed to\n", + "`workspace.xs` through `U1_central` and `U1_spin_orbit`. It is used as-is, so the\n", + "isovector factor is applied here explicitly. This comparison is only a demonstration\n", + "and is not checked against CHEX." + ] + }, { "cell_type": "code", "execution_count": 18, + "id": "815a719b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "E_mid = 31.34 MeV\n" + ] + } + ], + "source": [ + "E_mid = 0.5 * (kinematics_entrance.Elab + kinematics_exit.Elab)\n", + "kinematics_mid_p = reaction.kinematics(E_mid, relativistic=False)\n", + "kinematics_mid_n = reaction_exit_channel.kinematics(E_mid, relativistic=False)\n", + "\n", + "U_p_central_mid, U_p_spin_orbit_mid, _ = omp_entrance(\n", + " rgrid, reaction, kinematics_mid_p, *kd_default_proton_params\n", + ")\n", + "U_n_central_mid, U_n_spin_orbit_mid, _ = omp_exit(\n", + " rgrid, reaction_exit_channel, kinematics_mid_n, *kd_default_neutron_params\n", + ")\n", + "\n", + "U1_central_mid = -(U_n_central_mid - U_p_central_mid) * workspace.isovector_factor\n", + "U1_spin_orbit_mid = (\n", + " -(U_n_spin_orbit_mid - U_p_spin_orbit_mid) * workspace.isovector_factor\n", + ")\n", + "\n", + "xs_mid = workspace.xs(\n", + " U_p_coulomb,\n", + " U_p_central,\n", + " U_p_spin_orbit,\n", + " U_n_central,\n", + " U_n_spin_orbit,\n", + " U1_central=U1_central_mid,\n", + " U1_spin_orbit=U1_spin_orbit_mid,\n", + ")\n", + "print(f\"E_mid = {E_mid:.2f} MeV\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "id": "26813ce5-298e-4e6e-a767-391df2590723", "metadata": {}, "outputs": [], @@ -319,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "e07a8f8d-b8d5-48d5-a085-03dadb12fba2", "metadata": {}, "outputs": [], @@ -349,13 +411,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "a2b503d1-5468-46bf-a884-ff5d0c8a719d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -374,7 +436,12 @@ " marker=\".\",\n", ")\n", "\n", - "plt.plot(workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR\")\n", + "plt.plot(\n", + " workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR (channel-energy $U_1$)\"\n", + ")\n", + "plt.plot(\n", + " workspace.angles * 180 / np.pi, xs_mid, \":\", label=\"JITR (midpoint-energy $U_1$)\"\n", + ")\n", "plt.plot(xspn[\"theta\"], xspn[\"dxs\"], label=\"CHEX\", alpha=0.5)\n", "plt.xlabel(r\"$\\theta$ [deg]\")\n", "plt.ylabel(r\" $d \\sigma / \\Omega$ [mb/Sr]\")\n", @@ -385,13 +452,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "623d1c97-f9fe-42b5-bd6b-57375ad69834", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -410,7 +477,12 @@ " marker=\".\",\n", ")\n", "\n", - "plt.plot(workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR\")\n", + "plt.plot(\n", + " workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR (channel-energy $U_1$)\"\n", + ")\n", + "plt.plot(\n", + " workspace.angles * 180 / np.pi, xs_mid, \":\", label=\"JITR (midpoint-energy $U_1$)\"\n", + ")\n", "plt.plot(xspn[\"theta\"], xspn[\"dxs\"], label=\"CHEX\", alpha=0.5)\n", "plt.xlabel(r\"$\\theta$ [deg]\")\n", "plt.ylabel(r\" $d \\sigma / \\Omega$ [mb/Sr]\")\n", @@ -422,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "22e9e190-1f57-41aa-b521-62e2f8d718d9", "metadata": {}, "outputs": [], diff --git a/src/jitr/xs/quasielastic_pn.py b/src/jitr/xs/quasielastic_pn.py index dd03b0f8..31d814de 100644 --- a/src/jitr/xs/quasielastic_pn.py +++ b/src/jitr/xs/quasielastic_pn.py @@ -245,6 +245,8 @@ def tmatrix( U_p_spin_orbit: npt.ArrayLike | None = None, U_n_central: npt.ArrayLike | None = None, U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ Calculate the transition matrix for (p,n) quasi-elastic scattering @@ -256,6 +258,13 @@ def tmatrix( U_p_spin_orbit: Spin-orbit interaction for the proton. U_n_central: Central interaction for the neutron. U_n_spin_orbit: Spin-orbit interaction for the neutron. + U1_central: Central (p,n) transition potential on the quadrature + grid, used as-is in the radial integral. If None, defaults to + ``-(U_n_central - U_p_central) * isovector_factor``. + U1_spin_orbit: Spin-orbit (p,n) transition potential on the + quadrature grid, used as-is in the radial integral. If None, + defaults to + ``-(U_n_spin_orbit - U_p_spin_orbit) * isovector_factor``. Returns: Tuple (Tpn, Sn, Sp) where Tpn is the transition matrix for the @@ -321,10 +330,16 @@ def tmatrix( local_potential=neutron_spin_orbit, ) - U1_central = -(neutron_central - proton_central) * self.isovector_factor - U1_spin_orbit = ( - -(neutron_spin_orbit - proton_spin_orbit) * self.isovector_factor - ) + if U1_central is None: + U1_central = -(neutron_central - proton_central) * self.isovector_factor + else: + U1_central = self._local_potential(U1_central, "U1_central") + if U1_spin_orbit is None: + U1_spin_orbit = ( + -(neutron_spin_orbit - proton_spin_orbit) * self.isovector_factor + ) + else: + U1_spin_orbit = self._local_potential(U1_spin_orbit, "U1_spin_orbit") def tmatrix_element(l, ji, l_dot_s): nch = self.n_channels[l] @@ -385,6 +400,8 @@ def xs( U_p_spin_orbit: npt.ArrayLike | None = None, U_n_central: npt.ArrayLike | None = None, U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, ) -> np.ndarray: """ Calculate the differential cross section for (p,n) quasi-elastic @@ -396,6 +413,13 @@ def xs( U_p_spin_orbit: Spin-orbit interaction for the proton. U_n_central: Central interaction for the neutron. U_n_spin_orbit: Spin-orbit interaction for the neutron. + U1_central: Central (p,n) transition potential on the quadrature + grid, used as-is in the radial integral. If None, defaults to + ``-(U_n_central - U_p_central) * isovector_factor``. + U1_spin_orbit: Spin-orbit (p,n) transition potential on the + quadrature grid, used as-is in the radial integral. If None, + defaults to + ``-(U_n_spin_orbit - U_p_spin_orbit) * isovector_factor``. Returns: Differential cross section for the (p,n) reaction in mb/Sr. @@ -408,6 +432,8 @@ def xs( U_p_spin_orbit=U_p_spin_orbit, U_n_central=U_n_central, U_n_spin_orbit=U_n_spin_orbit, + U1_central=U1_central, + U1_spin_orbit=U1_spin_orbit, ) # TODO cast into a np.sum for im, m in enumerate([-0.5, 0.5]): diff --git a/tests/test_quasielastic_pn_spin_flip.py b/tests/test_quasielastic_pn_spin_flip.py index 758ba472..09eac95b 100644 --- a/tests/test_quasielastic_pn_spin_flip.py +++ b/tests/test_quasielastic_pn_spin_flip.py @@ -95,3 +95,71 @@ def test_spin_orbit_produces_spin_flip(workspace: Workspace) -> None: # spin-flip vanishes at 0 and 180 degrees but is sizable at intermediate angles mid = (workspace.angles > np.pi / 4) & (workspace.angles < 3 * np.pi / 4) assert np.mean(flip[mid] / xs[mid]) > 0.05 + + +def _default_U1(workspace: Workspace, potentials: dict[str, np.ndarray]): + f = workspace.isovector_factor + U1_central = -(potentials["U_n_central"] - potentials["U_p_central"]) * f + U1_spin_orbit = -(potentials["U_n_spin_orbit"] - potentials["U_p_spin_orbit"]) * f + return U1_central, U1_spin_orbit + + +def test_explicit_U1_matches_default(workspace: Workspace) -> None: + potentials = _potentials(workspace) + U1_central, U1_spin_orbit = _default_U1(workspace, potentials) + + default = workspace.tmatrix(**potentials) + explicit = workspace.tmatrix( + **potentials, U1_central=U1_central, U1_spin_orbit=U1_spin_orbit + ) + for d, e in zip(default, explicit, strict=True): + np.testing.assert_allclose(e, d, rtol=1e-12) + + np.testing.assert_allclose( + workspace.xs(**potentials, U1_central=U1_central, U1_spin_orbit=U1_spin_orbit), + workspace.xs(**potentials), + rtol=1e-12, + ) + + +def test_U1_defaults_are_independent(workspace: Workspace) -> None: + potentials = _potentials(workspace) + U1_central, _ = _default_U1(workspace, potentials) + + default = workspace.xs(**potentials) + only_central = workspace.xs(**potentials, U1_central=U1_central) + np.testing.assert_allclose(only_central, default, rtol=1e-12) + + no_so_transition = workspace.xs( + **potentials, + U1_central=U1_central, + U1_spin_orbit=np.zeros_like(U1_central), + ) + assert not np.allclose(no_so_transition, default, rtol=1e-3) + + +def test_U1_sets_transition_strength(workspace: Workspace) -> None: + potentials = _potentials(workspace) + U1_central, _ = _default_U1(workspace, potentials) + zero = np.zeros_like(U1_central) + + Tlj, Sn, Sp = workspace.tmatrix( + **potentials, U1_central=U1_central, U1_spin_orbit=zero + ) + Tlj2, Sn2, Sp2 = workspace.tmatrix( + **potentials, U1_central=2 * U1_central, U1_spin_orbit=zero + ) + np.testing.assert_allclose(Tlj2, 2 * Tlj, rtol=1e-12) + # distorted waves do not depend on the transition potential + np.testing.assert_allclose(Sn2, Sn) + np.testing.assert_allclose(Sp2, Sp) + + xs = workspace.xs(**potentials, U1_central=U1_central, U1_spin_orbit=zero) + xs2 = workspace.xs(**potentials, U1_central=2 * U1_central, U1_spin_orbit=zero) + np.testing.assert_allclose(xs2, 4 * xs, rtol=1e-12) + + +def test_U1_shape_is_validated(workspace: Workspace) -> None: + potentials = _potentials(workspace) + with pytest.raises(ValueError, match="U1_central"): + workspace.tmatrix(**potentials, U1_central=np.zeros(3)) From c19430137d0d95282297c46acc71d0fb830bfd6b Mon Sep 17 00:00:00 2001 From: beykyle Date: Fri, 18 Sep 2026 12:43:20 -0400 Subject: [PATCH 03/11] fix multichannel boundary matching for channels with different k and mu Channels in one partial wave share a physical channel radius but may have different wavenumbers, reduced masses and Sommerfeld parameters (e.g. the p and n channels of Lane (p,n)_IAS). Previously every channel's Coulomb-Hankel functions were evaluated at k_0 a, surface derivatives were taken in s = k_0 r without the mu_0/mu_i Bloch factor, and S was the raw amplitude ratio. - get_partial_wave_channels tabulates each channel at rho_i = k_i a, keyed by (eta, rho) - Solver.solve scales channel derivatives by v_i/v_0, returns the flux-normalized S and R in the rho-derivative convention All changes are the identity for a single channel or equal k and mu. Add tests: uncoupled different-k channels reproduce single-channel solves, unitary/symmetric S for real coupling, and channel-radius independence. --- src/jitr/reactions/system.py | 44 ++++++++++--- src/jitr/rmatrix/rmatrix.py | 56 ++++++++++++++-- tests/test_multichannel_asymptotics.py | 90 ++++++++++++++++++++++++++ 3 files changed, 175 insertions(+), 15 deletions(-) create mode 100644 tests/test_multichannel_asymptotics.py diff --git a/src/jitr/reactions/system.py b/src/jitr/reactions/system.py index d5ef6079..52f5c0df 100644 --- a/src/jitr/reactions/system.py +++ b/src/jitr/reactions/system.py @@ -52,10 +52,29 @@ def __init__( @classmethod def from_table( - cls, tables: dict[float, CoulombHankelTable], l: int, eta: FloatArray + cls, + tables: dict[tuple[float, float], CoulombHankelTable], + l: int, + eta: FloatArray, + rho: FloatArray, ) -> Asymptotics: - """Gather partial wave ``l`` for each channel's ``eta`` from tabulated functions.""" - rows = np.array([tables[float(e)][:] for e in eta], dtype=np.complex128) + """Gather partial wave ``l`` for each channel from tabulated functions. + + Args: + tables: Coulomb-Hankel tables keyed by ``(eta, rho)``. + l: Orbital angular momentum. + eta: Sommerfeld parameter of each channel. + rho: Channel radius ``k_i a`` of each channel, in that channel's + own dimensionless coordinate. + + Returns: + Asymptotic functions and their derivatives with respect to each + channel's own ``rho``. + """ + rows = np.array( + [tables[(float(e), float(r))][:] for e, r in zip(eta, rho, strict=True)], + dtype=np.complex128, + ) Hp, Hm, Hpp, Hmp = rows[:, :, l].T return cls(Hp=Hp, Hm=Hm, Hpp=Hpp, Hmp=Hmp) @@ -140,7 +159,10 @@ def __init__( """Store channel-independent parameters for each partial wave. Args: - channel_radius: Dimensionless channel radius ``k_0 r``. + channel_radius: Dimensionless channel radius ``k_0 a``, where + ``k_0`` is the wavenumber of the first channel. Every channel + shares the same physical radius ``a``, so channel ``i`` is + matched at ``k_i a``. lmax: Maximum orbital angular momentum. mass_target: Target mass in MeV/c^2. mass_projectile: Projectile mass in MeV/c^2. @@ -176,11 +198,9 @@ def get_partial_wave_channels( channels: list[Channels] = [] asymptotics: list[Asymptotics] = [] # Coulomb-Hankel functions and derivatives for all partial waves, tabulated - # once per distinct Sommerfeld parameter - tables = { - float(e): coulomb_hankel_table(self.channel_radius, float(e), self.lmax) - for e in np.unique(np.atleast_1d(np.asarray(eta, dtype=np.float64))) - } + # once per distinct (eta, rho). Every channel shares the physical channel + # radius a = channel_radius / k_0, so channel i is matched at rho_i = k_i a. + tables: dict[tuple[float, float], CoulombHankelTable] = {} for l in range(0, self.lmax + 1): num_channels = self.couplings[l].shape[0] eta_array = uniform_array_from_scalar_or_array(eta, num_channels) @@ -208,7 +228,11 @@ def get_partial_wave_channels( self.couplings[l], ) ) - asymptotics.append(Asymptotics.from_table(tables, l, eta_array)) + rho_array = self.channel_radius * k_array / k_array[0] + for key in zip(eta_array.tolist(), rho_array.tolist(), strict=True): + if key not in tables: + tables[key] = coulomb_hankel_table(key[1], key[0], self.lmax) + asymptotics.append(Asymptotics.from_table(tables, l, eta_array, rho_array)) return channels, asymptotics diff --git a/src/jitr/rmatrix/rmatrix.py b/src/jitr/rmatrix/rmatrix.py index e01bb6fe..2ea84179 100644 --- a/src/jitr/rmatrix/rmatrix.py +++ b/src/jitr/rmatrix/rmatrix.py @@ -168,7 +168,44 @@ def solve( weights: FloatArray | None = None, wavefunction: bool = False, ) -> tuple[np.ndarray, ...]: - """Solve the scattering problem for one coupled set of channels.""" + """Solve the scattering problem for one coupled set of channels. + + Channels may differ in wavenumber ``k_i``, reduced mass ``mu_i`` and + Sommerfeld parameter; they share one physical channel radius, and the + interior problem is solved on the grid ``s = k_0 r`` of channel 0. The + asymptotics must be evaluated at each channel's own ``rho_i = k_i a`` + (as :meth:`ProjectileTargetSystem.get_partial_wave_channels` does), with + derivatives with respect to ``rho_i``. + + Args: + channels: Channel data for one partial wave. + asymptotics: Coulomb-Hankel functions at each channel's radius. + local_potential: Local potential in MeV on the quadrature grid, + shape ``(nbasis,)`` or ``(nch, nch, nbasis)``. + nonlocal_potential: Nonlocal potential in MeV fm^-1 on the + quadrature grid. + interaction_matrix: Precomputed interaction matrix; overrides the + potentials. + free_matrix: Precomputed free matrix. + basis_boundary: Precomputed basis functions at the channel radius. + weights: Amplitude of the incoming wave in each channel; defaults + to channel 0 only. + wavefunction: If True, also return the interior expansion + coefficients. + + Returns: + ``(R, S, uext_prime_boundary)``, or + ``(R, S, coeffs, uext_prime_boundary)`` if ``wavefunction``. + ``S`` is the flux-normalized (unitary for real potentials) + S-matrix, ``S[i, j]`` the amplitude for outgoing channel ``i`` + given incoming channel ``j``. ``R`` satisfies + ``u_i(a) = sum_j R[i, j] rho_j du_j/drho_j`` at the channel + radius. ``uext_prime_boundary`` is the Bloch-surface source in the + solver's units. For a single channel, or channels with equal + ``k`` and ``mu``, these reduce to the usual single-grid + quantities. The ``coeffs`` correspond to raw (not flux-normalized) + incoming amplitudes ``weights``. + """ if free_matrix is None: free_matrix = self.free_matrix( channels.a, @@ -198,21 +235,30 @@ def solve( assert basis_boundary.shape == (self.kernel.quadrature.nbasis,) system_matrix = free_matrix + interaction_matrix + + # The interior is solved in s = k_0 r with kinetic terms scaled by + # mu_0 / mu_i, so channel i's surface derivative is + # (mu_0 / mu_i) d/ds = (k_i mu_0) / (k_0 mu_i) d/drho_i = (v_i / v_0) d/drho_i. + v_ratio = (channels.k * channels.mu[0]) / (channels.k[0] * channels.mu) R, S, inverse, uext_prime_boundary = solve_smatrix_with_inverse( system_matrix, basis_boundary, asymptotics.Hp, asymptotics.Hm, - asymptotics.Hpp, - asymptotics.Hmp, + asymptotics.Hpp * v_ratio, + asymptotics.Hmp * v_ratio, weights, channels.a, channels.size, self.kernel.quadrature.nbasis, ) + # R in the rho-derivative convention, and the flux-normalized S + R_rho = R * (channels.mu[0] / channels.mu)[np.newaxis, :] + S_flux = S * np.sqrt(v_ratio[:, np.newaxis] / v_ratio[np.newaxis, :]) + if not wavefunction: - return R, S, uext_prime_boundary + return R_rho, S_flux, uext_prime_boundary coeffs = solution_coeffs_with_inverse( inverse, @@ -222,4 +268,4 @@ def solve( channels.size, self.kernel.quadrature.nbasis, ) - return R, S, coeffs, uext_prime_boundary + return R_rho, S_flux, coeffs, uext_prime_boundary diff --git a/tests/test_multichannel_asymptotics.py b/tests/test_multichannel_asymptotics.py new file mode 100644 index 00000000..ed52ba3d --- /dev/null +++ b/tests/test_multichannel_asymptotics.py @@ -0,0 +1,90 @@ +"""Multichannel R-matrix solves with a different k, mu and eta in each channel. + +A proton-like channel (with Coulomb) and a neutron-like channel share one physical +channel radius in fm but have different wavenumbers, reduced masses and Sommerfeld +parameters, as in the Lane (p,n) isobaric-analog problem. +""" + +import numpy as np +import pytest + +from jitr import reactions, rmatrix +from jitr.optical_potentials.potential_forms import ( + coulomb_charged_sphere, + woods_saxon_safe, +) +from jitr.utils.constants import ALPHA, HBARC + +K = np.array([1.27, 1.13]) +MU = np.array([918.96, 920.21]) +ZZ = 20.0 +# the proton Sommerfeld parameter must match the interior Coulomb potential +ETA = np.array([ALPHA * ZZ * MU[0] / (HBARC * K[0]), 0.0]) +R_WS, A_WS, R_C = 4.4, 0.65, 4.7 + + +def _system(channel_radius_fm, k, mu, eta, lmax): + nch = np.size(k) + system = reactions.ProjectileTargetSystem( + channel_radius=channel_radius_fm * np.atleast_1d(k)[0], + lmax=lmax, + mass_target=44657.0, + mass_projectile=938.3, + coupling=lambda l: np.eye(nch), + ) + k, mu, eta = (np.atleast_1d(np.asarray(x, dtype=float)) for x in (k, mu, eta)) + return system.get_partial_wave_channels(0.0, 0.0, mu, k, eta) + + +def _potential(r, coupling, absorptive): + W = 8.0j if absorptive else 0.0 + V_p = (-50.0 - W) * woods_saxon_safe(r, R_WS, A_WS) + coulomb_charged_sphere( + r, ZZ, R_C + ) + V_n = (-46.0 - W) * woods_saxon_safe(r, R_WS, A_WS) + V_pn = coupling * woods_saxon_safe(r, R_WS, A_WS) + return np.array([[V_p, V_pn], [V_pn, V_n]], dtype=np.complex128) + + +def _coupled_S(solver, channel_radius_fm, l, coupling, absorptive=True): + channels, asymptotics = _system(channel_radius_fm, K, MU, ETA, l) + ch = channels[l] + r = solver.radial_grid(ch.a, ch.k[0]) + _, S, _ = solver.solve(ch, asymptotics[l], _potential(r, coupling, absorptive)) + return S + + +@pytest.mark.parametrize("l", [0, 1, 4]) +def test_uncoupled_channels_match_single_channel_solves(l): + solver = rmatrix.Solver(40) + S = _coupled_S(solver, 12.0, l, coupling=0.0) + + np.testing.assert_allclose(S[0, 1], 0, atol=1e-12) + np.testing.assert_allclose(S[1, 0], 0, atol=1e-12) + + for i in range(2): + channels, asymptotics = _system(12.0, K[i], MU[i], ETA[i], l) + ch = channels[l] + r = solver.radial_grid(ch.a, ch.k[0]) + V = _potential(r, 0.0, absorptive=True)[i, i] + _, S_single, _ = solver.solve(ch, asymptotics[l], V) + np.testing.assert_allclose(S[i, i], S_single[0, 0], rtol=1e-10) + + +@pytest.mark.parametrize("l", [0, 1, 4]) +def test_real_coupled_potential_gives_unitary_symmetric_S(l): + solver = rmatrix.Solver(40) + S = _coupled_S(solver, 12.0, l, coupling=-3.0, absorptive=False) + assert abs(S[1, 0]) > 1e-3 + np.testing.assert_allclose(S.conj().T @ S, np.eye(2), atol=1e-10) + np.testing.assert_allclose(S, S.T, atol=1e-10) + + +@pytest.mark.parametrize("l", [0, 3]) +def test_coupled_S_independent_of_channel_radius(l): + solver = rmatrix.Solver(60) + S_a = _coupled_S(solver, 12.0, l, coupling=-3.0) + S_b = _coupled_S(solver, 15.0, l, coupling=-3.0) + # a single-channel proton solve varies at the same ~1e-5 level between these + # radii (Woods-Saxon tail); before the multichannel boundary fix this was ~0.3 + np.testing.assert_allclose(S_a, S_b, atol=5e-5) From 24b8f381fc8bd9ad9673c57a03e08636c1a449e3 Mon Sep 17 00:00:00 2001 From: beykyle Date: Fri, 18 Sep 2026 12:48:06 -0400 Subject: [PATCH 04/11] add coupled-channels Lane (p,n)_IAS workspace jitr.xs.lane_pn.Workspace solves the 2x2 proton/neutron coupled channels for each (l, j), coupled by the isovector transition potential U1, with the incoming wave in the proton channel. It returns the full R- and flux-normalized S-matrices (rsmatrix) and builds the differential and angle-integrated (p,n) cross sections from S_np. The API mirrors the DWBA workspace, including the optional U1_central/U1_spin_orbit. Factor the potential validation, default U1 recipe, isovector factor and spin-1/2 CG/Y_lm transition geometry out of quasielastic_pn into shared helpers; DWBA output is unchanged (max rel diff 3e-15). Tests: CC(eps U1)/eps^2 converges to DWBA as O(eps^2), partial-wave and angle-integrated cross sections agree, S is unitary and symmetric for real potentials, j-independence without spin-orbit, default/explicit U1. --- src/jitr/xs/__init__.py | 4 +- src/jitr/xs/lane_pn.py | 316 +++++++++++++++++++++++++++++++++ src/jitr/xs/quasielastic_pn.py | 228 +++++++++++++++++------- tests/test_lane_pn.py | 122 +++++++++++++ 4 files changed, 600 insertions(+), 70 deletions(-) create mode 100644 src/jitr/xs/lane_pn.py create mode 100644 tests/test_lane_pn.py diff --git a/src/jitr/xs/__init__.py b/src/jitr/xs/__init__.py index 3114e06a..6821f6cd 100644 --- a/src/jitr/xs/__init__.py +++ b/src/jitr/xs/__init__.py @@ -1,5 +1,5 @@ """Cross-section workspaces and observable calculations.""" -from . import elastic, quasielastic_pn +from . import elastic, lane_pn, quasielastic_pn -__all__ = ["elastic", "quasielastic_pn"] +__all__ = ["elastic", "lane_pn", "quasielastic_pn"] diff --git a/src/jitr/xs/lane_pn.py b/src/jitr/xs/lane_pn.py new file mode 100644 index 00000000..bbdd030c --- /dev/null +++ b/src/jitr/xs/lane_pn.py @@ -0,0 +1,316 @@ +r"""Coupled-channels Lane workspace for quasi-elastic ``(p,n)`` to the IAS. + +The proton (entrance) and neutron (isobaric analog) channels are coupled by the +isovector transition potential :math:`U_1`. The Lane coupling is a scalar plus +a spin-orbit term, so it conserves :math:`l` and :math:`j`, and for each +:math:`(l, j)` the radial problem is the 2x2 system + +.. math:: + \left[T_l + U_{pp} - E_p\right] u_p + U_{1} u_n = 0, \qquad + \left[T_l + U_{nn} - E_n\right] u_n + U_{1} u_p = 0, + +with an incoming wave in the proton channel only. It is solved exactly with the +R-matrix method, which gives the full 2x2 R- and S-matrices. The first-order +(Born) approximation to the off-diagonal S-matrix element is the DWBA of +:mod:`jitr.xs.quasielastic_pn`. + +This is the minimal reference example of a coupled-channels calculation in +jitR: channels with different wavenumbers, reduced masses and Sommerfeld +parameters sharing one physical channel radius. +""" + +import numpy as np +import numpy.typing as npt +from scipy.special import gamma + +from ..reactions import ProjectileTargetSystem, Reaction, spin_half_orbit_coupling +from ..rmatrix import Solver +from ..utils.kinematics import ChannelKinematics +from .elastic import check_angles +from .quasielastic_pn import ( + isovector_factor, + pn_potentials, + spin_half_transition_geometry, +) + +ComplexArray = npt.NDArray[np.complex128] +FloatArray = npt.NDArray[np.float64] + +PROTON = 0 +NEUTRON = 1 + + +class Workspace: + r""" + Workspace for coupled-channels Lane (p,n) scattering to the isobaric + analog state. + + Channel 0 is the proton (entrance) channel and channel 1 the neutron + (exit) channel. Both share the channel radius ``channel_radius_fm``. + """ + + def __init__( + self, + reaction: Reaction, + kinematics_entrance: ChannelKinematics, + kinematics_exit: ChannelKinematics, + solver: Solver, + angles: FloatArray, + lmax: int, + channel_radius_fm: float, + ) -> None: + r""" + Initialize the coupled-channels (p,n) workspace. + + Args: + reaction: Reaction object containing information about the target, + projectile, residual, and product. + kinematics_entrance: Kinematics for the proton channel. + kinematics_exit: Kinematics for the neutron channel. + solver: R-matrix solver. + angles: Angles in radians at which to compute the differential + cross section. + lmax: The maximum orbital angular momentum. + channel_radius_fm: The channel radius in femtometers. + """ + if reaction.residual is None or reaction.product is None: + raise ValueError( + "Reaction must define both residual and product for (p,n) scattering" + ) + check_angles(angles) + + self.reaction = reaction + self.kinematics_entrance = kinematics_entrance + self.kinematics_exit = kinematics_exit + self.solver = solver + self.angles = angles + self.lmax = lmax + self.channel_radius_fm = channel_radius_fm + self.isovector_factor = isovector_factor(reaction) + + k = np.array([kinematics_entrance.k, kinematics_exit.k], dtype=np.float64) + mu = np.array([kinematics_entrance.mu, kinematics_exit.mu], dtype=np.float64) + eta = np.array([kinematics_entrance.eta, kinematics_exit.eta], dtype=np.float64) + + # one 2-channel (p, n) system per l; j enters only through l . sigma + self.sys = ProjectileTargetSystem( + channel_radius=channel_radius_fm * k[PROTON], + lmax=lmax, + mass_target=reaction.target.m0, + mass_projectile=reaction.projectile.m0, + Ztarget=reaction.target.Z, + Zproj=reaction.projectile.Z, + coupling=lambda l: np.eye(2), + ) + self.channels, self.asymptotics = self.sys.get_partial_wave_channels( + kinematics_entrance.Elab, kinematics_entrance.Ecm, mu, k, eta + ) + self.free_matrices = [ + np.asarray(self.solver.free_matrix(ch.a, ch.l, ch.E, ch.mu, coupled=True)) + for ch in self.channels + ] + self.basis_boundary = self.solver.precompute_boundaries(self.sys.channel_radius) + + # l . sigma for j = l + 1/2, l - 1/2 + self.l_dot_s = [np.diag(spin_half_orbit_coupling(l)) for l in range(lmax + 1)] + + # Coulomb phases; the neutron channel has eta = 0 + l = np.arange(lmax + 1) + self.sigma_c = np.angle(gamma(1 + l + 1j * eta[PROTON])) + np.angle( + gamma(1 + l + 1j * eta[NEUTRON]) + ) + self.geometric_factor = ( + np.sqrt(4 * np.pi) + / (2j * k[PROTON]) + * np.exp(1j * self.sigma_c)[:, np.newaxis, np.newaxis] + * spin_half_transition_geometry(lmax, angles) + ) + + def radial_grid(self) -> FloatArray: + """Return the physical quadrature grid used for local potentials.""" + return self.solver.radial_grid( + self.sys.channel_radius, self.kinematics_entrance.k + ) + + def _coupled_interaction_matrix( + self, Vpp: ComplexArray, Vnn: ComplexArray, Vpn: ComplexArray + ) -> ComplexArray: + """Interaction matrix for the symmetric 2x2 local potential.""" + ch = self.channels[0] + return self.solver.interaction_matrix( + ch.k[PROTON], + ch.E[PROTON], + ch.a, + ch.size, + local_potential=np.array([[Vpp, Vpn], [Vpn, Vnn]]), + ) + + def rsmatrix( + self, + U_p_coulomb: npt.ArrayLike, + U_p_central: npt.ArrayLike, + U_p_spin_orbit: npt.ArrayLike | None = None, + U_n_central: npt.ArrayLike | None = None, + U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, + ) -> tuple[ComplexArray, ComplexArray]: + """ + Solve the coupled (p,n) channels for every partial wave. + + Args: + U_p_coulomb: Coulomb interaction for the proton. + U_p_central: Central interaction for the proton. + U_p_spin_orbit: Spin-orbit interaction for the proton. + U_n_central: Central interaction for the neutron. + U_n_spin_orbit: Spin-orbit interaction for the neutron. + U1_central: Central (p,n) coupling potential on the quadrature + grid, used as-is. If None, defaults to + ``-(U_n_central - U_p_central) * isovector_factor``. + U1_spin_orbit: Spin-orbit (p,n) coupling potential on the + quadrature grid, used as-is. If None, defaults to + ``-(U_n_spin_orbit - U_p_spin_orbit) * isovector_factor``. + + Returns: + Tuple ``(R, S)`` of complex arrays with shape + ``(lmax + 1, 2, 2, 2)`` indexed by ``[l, j, out, in]``, where + ``j`` indexes ``(l + 1/2, l - 1/2)`` and channels are + ``(p, n)``. ``S`` is flux-normalized, so ``S[l, j, 1, 0]`` is the + (p,n) element. Entries for ``l = 0, j = l - 1/2`` are zero. + """ + potentials = pn_potentials( + self.solver.kernel.quadrature.nbasis, + self.isovector_factor, + U_p_coulomb, + U_p_central, + U_p_spin_orbit, + U_n_central, + U_n_spin_orbit, + U1_central, + U1_spin_orbit, + ) + # local interactions do not depend on l, and the spin-orbit part enters + # linearly with strength l . sigma, so build each piece once + im_central = self._coupled_interaction_matrix( + potentials["U_p_central"] + potentials["U_p_coulomb"], + potentials["U_n_central"], + potentials["U1_central"], + ) + im_spin_orbit = self._coupled_interaction_matrix( + potentials["U_p_spin_orbit"], + potentials["U_n_spin_orbit"], + potentials["U1_spin_orbit"], + ) + + R = np.zeros((self.lmax + 1, 2, 2, 2), dtype=np.complex128) + S = np.zeros((self.lmax + 1, 2, 2, 2), dtype=np.complex128) + for l in range(self.lmax + 1): + for ij, l_dot_s in enumerate(self.l_dot_s[l]): + R[l, ij], S[l, ij], _ = self.solver.solve( + self.channels[l], + self.asymptotics[l], + interaction_matrix=im_central + l_dot_s * im_spin_orbit, + free_matrix=self.free_matrices[l], + basis_boundary=self.basis_boundary, + ) + return R, S + + def xs( + self, + U_p_coulomb: npt.ArrayLike, + U_p_central: npt.ArrayLike, + U_p_spin_orbit: npt.ArrayLike | None = None, + U_n_central: npt.ArrayLike | None = None, + U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, + ) -> FloatArray: + """ + Differential (p,n) cross section in mb/sr in the outgoing neutron + angle, from the coupled-channels S-matrix. + + Args are as for :meth:`rsmatrix`. + + Returns: + Differential cross section at ``self.angles`` in mb/sr. + """ + _, S = self.rsmatrix( + U_p_coulomb, + U_p_central, + U_p_spin_orbit, + U_n_central, + U_n_spin_orbit, + U1_central, + U1_spin_orbit, + ) + return self.xs_from_smatrix(S) + + def integrated_xs( + self, + U_p_coulomb: npt.ArrayLike, + U_p_central: npt.ArrayLike, + U_p_spin_orbit: npt.ArrayLike | None = None, + U_n_central: npt.ArrayLike | None = None, + U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, + ) -> float: + """ + Angle-integrated (p,n) cross section in mb, from partial waves. + + Args are as for :meth:`rsmatrix`. + + Returns: + Integrated cross section in mb. + """ + _, S = self.rsmatrix( + U_p_coulomb, + U_p_central, + U_p_spin_orbit, + U_n_central, + U_n_spin_orbit, + U1_central, + U1_spin_orbit, + ) + return self.integrated_xs_from_smatrix(S) + + def xs_from_smatrix(self, S: ComplexArray) -> FloatArray: + r""" + Differential (p,n) cross section in mb/sr from the coupled S-matrix. + + .. math:: + f_{m m'}(\theta) = \frac{\sqrt{4\pi}}{2 i k_p} \sum_{lj} + \sqrt{2l+1}\langle l 0 \tfrac{1}{2} m | j m \rangle + \langle l, m-m'; \tfrac{1}{2} m' | j m \rangle + e^{i(\sigma_l^p + \sigma_l^n)} S^{lj}_{np} Y_l^{m-m'}(\theta, 0), + \qquad + \frac{d\sigma}{d\Omega} = \frac{1}{2}\sum_{m m'} |f_{m m'}|^2. + + Args: + S: Flux-normalized S-matrix from :meth:`rsmatrix`. + + Returns: + Differential cross section at ``self.angles`` in mb/sr. + """ + f = np.einsum("abljt,lj->abt", self.geometric_factor, S[:, :, NEUTRON, PROTON]) + return 10 * 0.5 * np.sum(np.abs(f) ** 2, axis=(0, 1)) + + def integrated_xs_from_smatrix(self, S: ComplexArray) -> float: + r""" + Angle-integrated (p,n) cross section in mb from the coupled S-matrix, + :math:`\sigma = \frac{\pi}{k_p^2}\sum_{lj} \frac{2j+1}{2}|S^{lj}_{np}|^2`. + + Args: + S: Flux-normalized S-matrix from :meth:`rsmatrix`. + + Returns: + Integrated cross section in mb. + """ + l = np.arange(self.lmax + 1)[:, np.newaxis] + two_j_plus_1 = np.hstack([2 * l + 2, 2 * l]) + return float( + 10 + * np.pi + / self.kinematics_entrance.k**2 + * np.sum(two_j_plus_1 / 2 * np.abs(S[:, :, NEUTRON, PROTON]) ** 2) + ) diff --git a/src/jitr/xs/quasielastic_pn.py b/src/jitr/xs/quasielastic_pn.py index 31d814de..6be59928 100644 --- a/src/jitr/xs/quasielastic_pn.py +++ b/src/jitr/xs/quasielastic_pn.py @@ -15,6 +15,138 @@ FloatArray = npt.NDArray[np.float64] +def isovector_factor(reaction: Reaction) -> float: + r"""Return :math:`\sqrt{|N-Z|}/(N-Z-1)` for the target of ``reaction``. + + This scales the difference of the neutron and proton optical potentials + into the default (p,n) transition potential. + """ + A = reaction.target.A + Z = reaction.target.Z + N = A - Z + return float(np.sqrt(np.fabs(N - Z)) / (N - Z - 1)) + + +def as_local_potential( + potential: npt.ArrayLike, nbasis: int, name: str +) -> ComplexArray: + """Validate and cast a local potential array on the quadrature grid.""" + potential_array = np.asarray(potential, dtype=np.complex128) + if potential_array.shape != (nbasis,): + raise ValueError(f"{name} must have shape {(nbasis,)}") + return potential_array + + +def as_optional_local_potential( + potential: npt.ArrayLike | None, nbasis: int, name: str +) -> ComplexArray: + """Return a validated local potential or a zero array when omitted.""" + if potential is None: + return np.zeros(nbasis, dtype=np.complex128) + return as_local_potential(potential, nbasis, name) + + +def pn_potentials( + nbasis: int, + isovector_factor: float, + U_p_coulomb: npt.ArrayLike, + U_p_central: npt.ArrayLike, + U_p_spin_orbit: npt.ArrayLike | None = None, + U_n_central: npt.ArrayLike | None = None, + U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, +) -> dict[str, ComplexArray]: + """Validate the (p,n) potentials and fill in the default transition terms. + + Args: + nbasis: Size of the quadrature grid. + isovector_factor: Scale applied to ``U_n - U_p`` in the default + transition potentials (see :func:`isovector_factor`). + U_p_coulomb: Coulomb interaction for the proton. + U_p_central: Central interaction for the proton. + U_p_spin_orbit: Spin-orbit interaction for the proton. + U_n_central: Central interaction for the neutron (required). + U_n_spin_orbit: Spin-orbit interaction for the neutron. + U1_central: Central transition potential, used as-is. Defaults to + ``-(U_n_central - U_p_central) * isovector_factor``. + U1_spin_orbit: Spin-orbit transition potential, used as-is. Defaults + to ``-(U_n_spin_orbit - U_p_spin_orbit) * isovector_factor``. + + Returns: + Validated complex arrays keyed by argument name; omitted spin-orbit + terms are zero. + """ + if U_n_central is None: + raise TypeError("U_n_central is required") + potentials = { + "U_p_coulomb": as_local_potential(U_p_coulomb, nbasis, "U_p_coulomb"), + "U_p_central": as_local_potential(U_p_central, nbasis, "U_p_central"), + "U_p_spin_orbit": as_optional_local_potential( + U_p_spin_orbit, nbasis, "U_p_spin_orbit" + ), + "U_n_central": as_local_potential(U_n_central, nbasis, "U_n_central"), + "U_n_spin_orbit": as_optional_local_potential( + U_n_spin_orbit, nbasis, "U_n_spin_orbit" + ), + } + if U1_central is None: + potentials["U1_central"] = ( + -(potentials["U_n_central"] - potentials["U_p_central"]) * isovector_factor + ) + else: + potentials["U1_central"] = as_local_potential(U1_central, nbasis, "U1_central") + if U1_spin_orbit is None: + potentials["U1_spin_orbit"] = ( + -(potentials["U_n_spin_orbit"] - potentials["U_p_spin_orbit"]) + * isovector_factor + ) + else: + potentials["U1_spin_orbit"] = as_local_potential( + U1_spin_orbit, nbasis, "U1_spin_orbit" + ) + return potentials + + +def spin_half_transition_geometry(lmax: int, angles: FloatArray) -> ComplexArray: + r"""Angular factors for a spin-1/2 transition on a spin-0 target. + + For a transition that conserves :math:`l` and :math:`j`, the amplitude + for projectile spin projection :math:`m \to m'` is a sum over partial + waves of + + .. math:: + \sqrt{2l+1} \langle l 0 \tfrac{1}{2} m | j m \rangle + \langle l, m-m'; \tfrac{1}{2} m' | j m \rangle Y_l^{m-m'}(\theta, 0) + + times a partial-wave amplitude. + + Args: + lmax: Maximum orbital angular momentum. + angles: Scattering angles in radians. + + Returns: + Array of shape ``(2, 2, lmax + 1, 2, len(angles))`` indexed by + ``[m, m', l, j]``, with ``m, m'`` in ``(-1/2, +1/2)`` and ``j`` in + ``(l + 1/2, l - 1/2)``. Entries with no allowed ``j`` are zero. + """ + geometry = np.zeros((2, 2, lmax + 1, 2, angles.shape[0]), dtype=np.complex128) + for im, m in enumerate([-0.5, 0.5]): + for imp, mp in enumerate([-0.5, 0.5]): + for l in range(0, lmax + 1): + for ijp, jp in enumerate( + [l + 1 / 2, l - 1 / 2] if l > 0 else [l + 1 / 2] + ): + if abs(m - mp) <= l: + ylm = sph_harm_y(l, int(m - mp), angles, 0) + cg0 = float(clebsch_gordan(l, 1 / 2, jp, m - mp, mp, m)) + cg1 = float(clebsch_gordan(l, 1 / 2, jp, 0, m, m)) + geometry[im, imp, l, ijp, :] = ( + cg1 * cg0 * np.sqrt(2 * l + 1) * ylm + ) + return geometry + + class System: r""" System for (p,n) quasi-elastic scattering observables for local interactions @@ -141,10 +273,7 @@ def __init__( self.angles = angles # precompute for DWBA matrix element - A = self.reaction.target.A - Z = self.reaction.target.Z - N = A - Z - self.isovector_factor = np.sqrt(np.fabs(N - Z)) / (N - Z - 1) + self.isovector_factor = isovector_factor(self.reaction) # precompute things for entrance channel self.free_matrices_p = self.solver.free_matrix( @@ -188,33 +317,16 @@ def __init__( * self.kinematics_exit.mu / (4 * np.pi**2 * constants.HBARC**4 * (2 * 1.0 / 2 + 1)) ) - self.geometric_factor = np.zeros( - (2, 2, self.sys.lmax + 1, 2, self.angles.shape[0]), dtype=np.complex128 - ) self.sigma_c = np.angle( gamma(1 + self.sys.l + 1j * self.kinematics_entrance.eta) ) - for im, m in enumerate([-0.5, 0.5]): - for imp, mp in enumerate([-0.5, 0.5]): - for l in range(0, self.sys.lmax + 1): - for ijp, jp in enumerate( - [l + 1 / 2, l - 1 / 2] if l > 0 else [l + 1 / 2] - ): - if abs(m - mp) <= l and jp >= 0: - ylm = sph_harm_y(l, int(m - mp), self.angles, 0) - cg0 = clebsch_gordan(l, 1 / 2, jp, m - mp, mp, m) - cg1 = clebsch_gordan(l, 1 / 2, jp, 0, m, m) - - self.geometric_factor[im, imp, l, ijp, :] = ( - (4 * np.pi) ** (3.0 / 2.0) - / (self.kinematics_entrance.k * self.kinematics_exit.k) - * np.exp(1j * self.sigma_c[l]) - * cg1 - * cg0 - * np.sqrt(2 * l + 1) - * (-1) ** (2 * jp + 1) - * ylm - ) + # (-1)^(2j+1) = 1 for half-integer j + self.geometric_factor = ( + (4 * np.pi) ** (3.0 / 2.0) + / (self.kinematics_entrance.k * self.kinematics_exit.k) + * np.exp(1j * self.sigma_c)[:, np.newaxis, np.newaxis] + * spin_half_transition_geometry(self.sys.lmax, self.angles) + ) def radial_grid(self) -> FloatArray: """Return the physical quadrature grid used for local potentials.""" @@ -222,22 +334,6 @@ def radial_grid(self) -> FloatArray: self.p_channels[0][0].a, self.kinematics_entrance.k ) - def _local_potential(self, potential: npt.ArrayLike, name: str) -> ComplexArray: - """Validate and cast a local potential array on the quadrature grid.""" - potential_array = np.asarray(potential, dtype=np.complex128) - expected_shape = (self.solver.kernel.quadrature.nbasis,) - if potential_array.shape != expected_shape: - raise ValueError(f"{name} must have shape {expected_shape}") - return potential_array - - def _optional_local_potential( - self, potential: npt.ArrayLike | None, name: str - ) -> ComplexArray: - """Return a validated local potential or a zero array when omitted.""" - if potential is None: - return np.zeros(self.solver.kernel.quadrature.nbasis, dtype=np.complex128) - return self._local_potential(potential, name) - def tmatrix( self, U_p_coulomb: npt.ArrayLike, @@ -276,20 +372,27 @@ def tmatrix( Sn = np.zeros((self.sys.lmax + 1, 2), dtype=np.complex128) Sp = np.zeros((self.sys.lmax + 1, 2), dtype=np.complex128) + potentials = pn_potentials( + self.solver.kernel.quadrature.nbasis, + self.isovector_factor, + U_p_coulomb, + U_p_central, + U_p_spin_orbit, + U_n_central, + U_n_spin_orbit, + U1_central, + U1_spin_orbit, + ) + proton_central = potentials["U_p_central"] + proton_spin_orbit = potentials["U_p_spin_orbit"] + proton_coulomb = potentials["U_p_coulomb"] + neutron_central = potentials["U_n_central"] + neutron_spin_orbit = potentials["U_n_spin_orbit"] + transition_central = potentials["U1_central"] + transition_spin_orbit = potentials["U1_spin_orbit"] + # precomute central, spin-obit, and Coulomb interaction matrices # for entrance channel distorted waves - if U_n_central is None: - raise TypeError("U_n_central is required") - - proton_central = self._local_potential(U_p_central, "U_p_central") - proton_spin_orbit = self._optional_local_potential( - U_p_spin_orbit, "U_p_spin_orbit" - ) - proton_coulomb = self._local_potential(U_p_coulomb, "U_p_coulomb") - neutron_central = self._local_potential(U_n_central, "U_n_central") - neutron_spin_orbit = self._optional_local_potential( - U_n_spin_orbit, "U_n_spin_orbit" - ) im_central_p = self.solver.interaction_matrix( self.p_channels[0][0].k[0], @@ -330,17 +433,6 @@ def tmatrix( local_potential=neutron_spin_orbit, ) - if U1_central is None: - U1_central = -(neutron_central - proton_central) * self.isovector_factor - else: - U1_central = self._local_potential(U1_central, "U1_central") - if U1_spin_orbit is None: - U1_spin_orbit = ( - -(neutron_spin_orbit - proton_spin_orbit) * self.isovector_factor - ) - else: - U1_spin_orbit = self._local_potential(U1_spin_orbit, "U1_spin_orbit") - def tmatrix_element(l, ji, l_dot_s): nch = self.n_channels[l] pch = self.p_channels[l] @@ -369,7 +461,7 @@ def tmatrix_element(l, ji, l_dot_s): ) tlj = ( - np.sum(xp * (U1_central + l_dot_s * U1_spin_orbit) * xn) + np.sum(xp * (transition_central + l_dot_s * transition_spin_orbit) * xn) / self.sys.channel_radius_fm / self.kinematics_entrance.k / self.kinematics_exit.k diff --git a/tests/test_lane_pn.py b/tests/test_lane_pn.py new file mode 100644 index 00000000..d45c7c82 --- /dev/null +++ b/tests/test_lane_pn.py @@ -0,0 +1,122 @@ +import numpy as np +import pytest + +from jitr.optical_potentials.potential_forms import ( + coulomb_charged_sphere, + woods_saxon_safe, +) +from jitr.reactions import Reaction +from jitr.rmatrix import Solver +from jitr.xs import lane_pn, quasielastic_pn + +ANGLES = np.linspace(1e-3, np.pi - 1e-3, 721) +LMAX = 15 +RADIUS = 14.0 + + +def _thomas(r, depth, R, a): + x = np.exp((r - R) / a) + return -depth * x / (1 + x) ** 2 / (a * r) + + +@pytest.fixture(scope="module") +def setup(): + reaction = Reaction((48, 20), (1, 1), (1, 0), (48, 21)) + ke = reaction.kinematics(35.0, relativistic=False) + kx = reaction.kinematics_exit(ke, 6.67, relativistic=False) + solver = Solver(35) + cc = lane_pn.Workspace(reaction, ke, kx, solver, ANGLES, LMAX, RADIUS) + dwba = quasielastic_pn.Workspace( + reaction, ke, kx, solver, ANGLES, LMAX, RADIUS, tmatrix_abs_tol=0 + ) + return cc, dwba + + +def _potentials(cc, absorptive=True, spin_orbit=True): + r = cc.radial_grid() + W = 8.0j if absorptive else 0.0 + so = 1.0 if spin_orbit else 0.0 + return { + "U_p_coulomb": coulomb_charged_sphere(r, 20, 4.7), + "U_p_central": (-50.0 - W) * woods_saxon_safe(r, 4.4, 0.65), + "U_p_spin_orbit": so * _thomas(r, 6.0, 4.0, 0.6), + "U_n_central": (-46.0 - W) * woods_saxon_safe(r, 4.4, 0.65), + "U_n_spin_orbit": so * _thomas(r, 5.5, 4.0, 0.6), + } + + +def _default_U1(ws, p): + f = ws.isovector_factor + return ( + -(p["U_n_central"] - p["U_p_central"]) * f, + -(p["U_n_spin_orbit"] - p["U_p_spin_orbit"]) * f, + ) + + +def test_grids_match(setup): + cc, dwba = setup + np.testing.assert_allclose(cc.radial_grid(), dwba.radial_grid()) + + +def test_weak_coupling_limit_is_dwba(setup): + # the Born approximation to the coupled-channels S_np is the DWBA, so + # CC(eps U1) / eps^2 -> DWBA(U1) with a relative error O(eps^2) + cc, dwba = setup + p = _potentials(cc) + U1_central, U1_spin_orbit = _default_U1(cc, p) + xs_dwba = dwba.xs(**p) + for eps in (1e-2, 1e-3): + xs_cc = ( + cc.xs(**p, U1_central=eps * U1_central, U1_spin_orbit=eps * U1_spin_orbit) + / eps**2 + ) + np.testing.assert_allclose(xs_cc, xs_dwba, rtol=50 * eps**2) + + +def test_integrated_xs_matches_angular_integral(setup): + cc, _ = setup + p = _potentials(cc) + _, S = cc.rsmatrix(**p) + dsdo = cc.xs_from_smatrix(S) + sigma_angular = 2 * np.pi * np.trapezoid(dsdo * np.sin(ANGLES), ANGLES) + np.testing.assert_allclose( + sigma_angular, cc.integrated_xs_from_smatrix(S), rtol=1e-4 + ) + np.testing.assert_allclose(cc.integrated_xs(**p), cc.integrated_xs_from_smatrix(S)) + + +def test_real_potentials_give_unitary_symmetric_S(setup): + cc, _ = setup + _, S = cc.rsmatrix(**_potentials(cc, absorptive=False)) + assert np.max(np.abs(S[:, :, 1, 0])) > 1e-3 + for l in range(LMAX + 1): + for j in range(2 if l > 0 else 1): + np.testing.assert_allclose(S[l, j].conj().T @ S[l, j], np.eye(2), atol=1e-9) + np.testing.assert_allclose(S[l, j], S[l, j].T, atol=1e-9) + + +def test_no_spin_orbit_is_j_independent(setup): + cc, _ = setup + R, S = cc.rsmatrix(**_potentials(cc, spin_orbit=False)) + np.testing.assert_allclose(S[1:, 0], S[1:, 1], atol=1e-12) + np.testing.assert_allclose(R[1:, 0], R[1:, 1], atol=1e-12) + np.testing.assert_array_equal(S[0, 1], 0) + + +def test_explicit_default_U1_matches_default(setup): + cc, _ = setup + p = _potentials(cc) + U1_central, U1_spin_orbit = _default_U1(cc, p) + np.testing.assert_allclose( + cc.xs(**p, U1_central=U1_central, U1_spin_orbit=U1_spin_orbit), + cc.xs(**p), + rtol=1e-12, + ) + + +def test_neutron_central_required(setup): + cc, _ = setup + p = _potentials(cc) + p.pop("U_n_central") + with pytest.raises(TypeError, match="U_n_central"): + cc.rsmatrix(**p) From 70e7d005f24280f77c5433eaebf93f6c56b5eddf Mon Sep 17 00:00:00 2001 From: beykyle Date: Fri, 18 Sep 2026 12:49:03 -0400 Subject: [PATCH 05/11] compare DWBA and coupled-channels Lane (p,n) in CHEX validation notebook Add a section computing the Lane coupled-channels (p,n)_IAS cross section with jitr.xs.lane_pn.Workspace, with the same potentials and kinematics as the DWBA, plotted against DWBA, CHEX and data (not asserted against CHEX). For 48Ca(p,n) at 35 MeV, CC is ~1% below DWBA (sigma_pn 7.55 vs 7.65 mb). --- examples/notebooks/chex_jitr_validation.ipynb | 110 ++++++++++++++++-- 1 file changed, 103 insertions(+), 7 deletions(-) diff --git a/examples/notebooks/chex_jitr_validation.ipynb b/examples/notebooks/chex_jitr_validation.ipynb index 244282aa..b5b13516 100644 --- a/examples/notebooks/chex_jitr_validation.ipynb +++ b/examples/notebooks/chex_jitr_validation.ipynb @@ -417,7 +417,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -436,9 +436,7 @@ " marker=\".\",\n", ")\n", "\n", - "plt.plot(\n", - " workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR (channel-energy $U_1$)\"\n", - ")\n", + "plt.plot(workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR (channel-energy $U_1$)\")\n", "plt.plot(\n", " workspace.angles * 180 / np.pi, xs_mid, \":\", label=\"JITR (midpoint-energy $U_1$)\"\n", ")\n", @@ -477,9 +475,7 @@ " marker=\".\",\n", ")\n", "\n", - "plt.plot(\n", - " workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR (channel-energy $U_1$)\"\n", - ")\n", + "plt.plot(workspace.angles * 180 / np.pi, xs, \"--\", label=\"JITR (channel-energy $U_1$)\")\n", "plt.plot(\n", " workspace.angles * 180 / np.pi, xs_mid, \":\", label=\"JITR (midpoint-energy $U_1$)\"\n", ")\n", @@ -502,6 +498,106 @@ "xs_interp = np.interp(xspn[\"theta\"], workspace.angles * 180 / np.pi, xs)\n", "np.testing.assert_allclose(xspn[\"dxs\"], xs_interp, rtol=0.05, atol=0.003)" ] + }, + { + "cell_type": "markdown", + "id": "e357476f", + "metadata": {}, + "source": [ + "### DWBA vs. coupled-channels Lane model\n", + "\n", + "In the Lane model, the proton and neutron (isobaric analog) channels are coupled by\n", + "the same isovector potential $U_1$ that drives the DWBA transition. The\n", + "coupled-channels (CC) workspace `jitr.xs.lane_pn.Workspace` solves the full $2\\times2$\n", + "problem for each $(l, j)$, with an incoming wave in the proton channel. From the\n", + "flux-normalized S-matrix it computes the $(p,n)$ cross section.\n", + "\n", + "The DWBA is the first-order (Born) limit of the CC solution in $U_1$. CC adds\n", + "back-coupling to the entrance channel and higher-order terms in $U_1$. The\n", + "diagonal potentials here are the global KDUQ optical potentials, which are fit to\n", + "elastic data. They may already implicitly contain part of the effect of this\n", + "coupling, so the CC result can double count it. This comparison is not checked\n", + "against CHEX." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "1e7e69bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sigma_pn DWBA: 7.647 mb CC: 7.553 mb\n", + "peak ratio CC/DWBA: 0.989\n" + ] + } + ], + "source": [ + "workspace_cc = jitr.xs.lane_pn.Workspace(\n", + " reaction,\n", + " kinematics_entrance,\n", + " kinematics_exit,\n", + " core_solver,\n", + " angles,\n", + " lmax,\n", + " channel_radius_fm,\n", + ")\n", + "R_cc, S_cc = workspace_cc.rsmatrix(\n", + " U_p_coulomb,\n", + " U_p_central,\n", + " U_p_spin_orbit,\n", + " U_n_central,\n", + " U_n_spin_orbit,\n", + ")\n", + "xs_cc = workspace_cc.xs_from_smatrix(S_cc)\n", + "\n", + "sigma_cc = workspace_cc.integrated_xs_from_smatrix(S_cc)\n", + "sigma_dwba = 2 * np.pi * np.trapezoid(xs * np.sin(angles), angles)\n", + "print(f\"sigma_pn DWBA: {sigma_dwba:.3f} mb CC: {sigma_cc:.3f} mb\")\n", + "print(f\"peak ratio CC/DWBA: {xs_cc.max() / xs.max():.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a3c0dceb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "for ax in axes:\n", + " ax.errorbar(\n", + " ca48_pn_ias[:, 0],\n", + " ca48_pn_ias[:, 1],\n", + " yerr=ca48_pn_ias[:, 2],\n", + " label=\"Jon et al., \",\n", + " linestyle=\"none\",\n", + " marker=\".\",\n", + " )\n", + " ax.plot(xspn[\"theta\"], xspn[\"dxs\"], label=\"CHEX (DWBA)\", alpha=0.5)\n", + " ax.plot(angles * 180 / np.pi, xs, \"--\", label=\"JITR DWBA\")\n", + " ax.plot(angles * 180 / np.pi, xs_cc, \":\", label=\"JITR coupled channels\")\n", + " ax.set_xlabel(r\"$\\theta$ [deg]\")\n", + " ax.set_ylabel(r\"$d \\sigma / d\\Omega$ [mb/Sr]\")\n", + "axes[1].set_yscale(\"log\")\n", + "axes[0].legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] } ], "metadata": { From cc22fd09416fb383fb74af534e42046d1f435cb2 Mon Sep 17 00:00:00 2001 From: beykyle Date: Mon, 21 Sep 2026 19:34:03 -0400 Subject: [PATCH 06/11] fix multichannel Bloch source and wavefunction reconstruction Review of the coupled-channels branch found that solve(..., wavefunction=True) returned wrong interior coefficients for every multichannel solve: the outgoing term in the Bloch surface source contracted the S-matrix with the channel index instead of the incoming weights. Interior and exterior wavefunctions disagreed by O(100%); single-channel results were unaffected. Wavefunctions.uext() also raised TypeError unconditionally (Channels has no __len__), passed a float l to the Coulomb functions, evaluated every channel in the channel-0 coordinate, and used the flux-normalized S in the raw-S asymptotic form. All fixed; it now takes the S returned by solve. Add tests/test_wavefunction_continuity.py pinning interior/exterior continuity at the channel radius for 1 and 3 channels with several incoming weights, and for two channels with different k and mu. Also from review: raise on the N - Z = 1 divergence of isovector_factor, replace the four-deep selection loop in quasielastic_pn.xs with the einsum it was equivalent to (DWBA output unchanged, 1.5e-15), hoist Y_lm out of the j loop, name the (eta, rho) table keys, and document the unused Elab/Ecm parameters of get_partial_wave_channels. --- src/jitr/reactions/system.py | 25 +++++-- src/jitr/reactions/wavefunction.py | 44 ++++++++----- src/jitr/rmatrix/core.py | 8 ++- src/jitr/xs/lane_pn.py | 1 + src/jitr/xs/quasielastic_pn.py | 31 ++++----- tests/test_wavefunction_continuity.py | 94 +++++++++++++++++++++++++++ 6 files changed, 164 insertions(+), 39 deletions(-) create mode 100644 tests/test_wavefunction_continuity.py diff --git a/src/jitr/reactions/system.py b/src/jitr/reactions/system.py index 52f5c0df..c4c04c9c 100644 --- a/src/jitr/reactions/system.py +++ b/src/jitr/reactions/system.py @@ -194,7 +194,20 @@ def get_partial_wave_channels( k: float | FloatArray, eta: float | FloatArray, ) -> tuple[list[Channels], list[Asymptotics]]: - """Build channel and asymptotic objects for every partial wave.""" + """Build channel and asymptotic objects for every partial wave. + + Args: + Elab: Unused; kept so that a :class:`ChannelKinematics` can be + unpacked directly into this call. + Ecm: Unused; the channel energy is ``hbar^2 k^2 / (2 mu)``. + mu: Reduced mass of each channel, or one value for all. + k: Wavenumber of each channel, or one value for all. + eta: Sommerfeld parameter of each channel, or one value for all. + + Returns: + One :class:`Channels` and one :class:`Asymptotics` per partial + wave, with channel ``i`` matched at ``rho_i = k_i a``. + """ channels: list[Channels] = [] asymptotics: list[Asymptotics] = [] # Coulomb-Hankel functions and derivatives for all partial waves, tabulated @@ -229,9 +242,13 @@ def get_partial_wave_channels( ) ) rho_array = self.channel_radius * k_array / k_array[0] - for key in zip(eta_array.tolist(), rho_array.tolist(), strict=True): - if key not in tables: - tables[key] = coulomb_hankel_table(key[1], key[0], self.lmax) + for eta_i, rho_i in zip( + eta_array.tolist(), rho_array.tolist(), strict=True + ): + if (eta_i, rho_i) not in tables: + tables[(eta_i, rho_i)] = coulomb_hankel_table( + rho=rho_i, eta=eta_i, lmax=self.lmax + ) asymptotics.append(Asymptotics.from_table(tables, l, eta_array, rho_array)) return channels, asymptotics diff --git a/src/jitr/reactions/wavefunction.py b/src/jitr/reactions/wavefunction.py index c168bcea..a6e9ff5a 100644 --- a/src/jitr/reactions/wavefunction.py +++ b/src/jitr/reactions/wavefunction.py @@ -36,30 +36,40 @@ def __init__( self.incoming_weights = incoming_weights def uext(self) -> list[Callable[[npt.ArrayLike], ComplexArray]]: - """Return external-channel wavefunctions valid beyond the boundary.""" + """Return external-channel wavefunctions valid beyond the boundary. + + The returned callables take the channel-0 coordinate ``s = k_0 r``; + channel ``i`` is evaluated at its own ``rho_i = k_i r``. ``S`` is taken + to be the flux-normalized matrix returned by :meth:`Solver.solve`. + """ + # amplitude of the outgoing wave in each channel, in the raw (not + # flux-normalized) convention that the asymptotic forms use + velocity = self.channels.k / self.channels.mu + outgoing = ( + self.S * np.sqrt(velocity[np.newaxis, :] / velocity[:, np.newaxis]) + ) @ self.incoming_weights.astype(np.complex128) + k_ratio = self.channels.k / self.channels.k[0] def uext_channel(i: int) -> Callable[[npt.ArrayLike], ComplexArray]: - l = self.channels.l[i] - eta = self.channels.eta[i] - - def asym_func_in(s: float) -> complex: - return self.incoming_weights[i] * H_minus(s, l, eta) - - def asym_func_out(s: float) -> complex: - return np.sum( - [ - self.incoming_weights[j] * self.S[i, j] * H_plus(s, l, eta) - for j in range(len(self.channels)) - ], - axis=0, + l = int(self.channels.l[i]) + eta = float(self.channels.eta[i]) + + def u(s: float) -> complex: + rho = s * k_ratio[i] + return ( + 1j + / 2 + * ( + self.incoming_weights[i] * H_minus(rho, l, eta) + - outgoing[i] * H_plus(rho, l, eta) + ) ) return lambda s_mesh: np.array( - [1j / 2 * (asym_func_in(s) - asym_func_out(s)) for s in s_mesh], - dtype=np.complex128, + [u(s) for s in np.atleast_1d(s_mesh)], dtype=np.complex128 ) - return [uext_channel(i) for i in range(len(self.channels))] + return [uext_channel(i) for i in range(self.channels.size)] def uint(self) -> list[Callable[[float], complex]]: """Return internal wavefunctions expanded in the Lagrange basis.""" diff --git a/src/jitr/rmatrix/core.py b/src/jitr/rmatrix/core.py index a3f6d8a6..f5cbd25e 100644 --- a/src/jitr/rmatrix/core.py +++ b/src/jitr/rmatrix/core.py @@ -55,7 +55,13 @@ def solve_smatrix_with_inverse( # Eqn 16 in Descouvemont, 2016 S = np.linalg.solve(Zp, Zm) - uext_prime_boundary = 1j / 2 * (Hmp * incoming_weights - S @ np.copy(Hpp)) + # derivative of u_i(s) = i/2 [ w_i H^-_i(s) - H^+_i(s) sum_j S_ij w_j ]; the + # outgoing wave carries the channel index i, and S contracts with the weights + uext_prime_boundary = ( + 1j + / 2 + * (Hmp * incoming_weights - Hpp * (S @ incoming_weights.astype(np.complex128))) + ) return R, S, Ainv, uext_prime_boundary diff --git a/src/jitr/xs/lane_pn.py b/src/jitr/xs/lane_pn.py index bbdd030c..1e1a2abd 100644 --- a/src/jitr/xs/lane_pn.py +++ b/src/jitr/xs/lane_pn.py @@ -102,6 +102,7 @@ def __init__( Zproj=reaction.projectile.Z, coupling=lambda l: np.eye(2), ) + # Elab and Ecm are unused; the per-channel energy is hbar^2 k^2 / (2 mu) self.channels, self.asymptotics = self.sys.get_partial_wave_channels( kinematics_entrance.Elab, kinematics_entrance.Ecm, mu, k, eta ) diff --git a/src/jitr/xs/quasielastic_pn.py b/src/jitr/xs/quasielastic_pn.py index 6be59928..8555eef5 100644 --- a/src/jitr/xs/quasielastic_pn.py +++ b/src/jitr/xs/quasielastic_pn.py @@ -24,6 +24,11 @@ def isovector_factor(reaction: Reaction) -> float: A = reaction.target.A Z = reaction.target.Z N = A - Z + if N - Z == 1: + raise ValueError( + f"the (p,n) isovector factor diverges for N - Z = 1 targets like " + f"{reaction.target}; supply U1_central and U1_spin_orbit explicitly" + ) return float(np.sqrt(np.fabs(N - Z)) / (N - Z - 1)) @@ -134,16 +139,15 @@ def spin_half_transition_geometry(lmax: int, angles: FloatArray) -> ComplexArray for im, m in enumerate([-0.5, 0.5]): for imp, mp in enumerate([-0.5, 0.5]): for l in range(0, lmax + 1): + if abs(m - mp) > l: + continue + ylm = sph_harm_y(l, int(m - mp), angles, 0) for ijp, jp in enumerate( [l + 1 / 2, l - 1 / 2] if l > 0 else [l + 1 / 2] ): - if abs(m - mp) <= l: - ylm = sph_harm_y(l, int(m - mp), angles, 0) - cg0 = float(clebsch_gordan(l, 1 / 2, jp, m - mp, mp, m)) - cg1 = float(clebsch_gordan(l, 1 / 2, jp, 0, m, m)) - geometry[im, imp, l, ijp, :] = ( - cg1 * cg0 * np.sqrt(2 * l + 1) * ylm - ) + cg0 = float(clebsch_gordan(l, 1 / 2, jp, m - mp, mp, m)) + cg1 = float(clebsch_gordan(l, 1 / 2, jp, 0, m, m)) + geometry[im, imp, l, ijp, :] = cg1 * cg0 * np.sqrt(2 * l + 1) * ylm return geometry @@ -517,7 +521,6 @@ def xs( Differential cross section for the (p,n) reaction in mb/Sr. """ - Tmmp = np.zeros((2, 2, self.angles.shape[0]), dtype=np.complex128) Tlj, Sn, Sp = self.tmatrix( U_p_coulomb=U_p_coulomb, U_p_central=U_p_central, @@ -527,13 +530,7 @@ def xs( U1_central=U1_central, U1_spin_orbit=U1_spin_orbit, ) - # TODO cast into a np.sum - for im, m in enumerate([-0.5, 0.5]): - for imp, mp in enumerate([-0.5, 0.5]): - for l in range(0, self.sys.lmax + 1): - for ijp, jp in enumerate([l + 0.5, l - 0.5]): - if abs(m - mp) <= l and jp >= 0: - Tmmp[im, imp, :] += ( - self.geometric_factor[im, imp, l, ijp, :] * Tlj[l, ijp] - ) + # geometric_factor is zero wherever the (l, j, m, m') combination is + # not allowed, so the sum needs no further selection rules + Tmmp = np.einsum("abljt,lj->abt", self.geometric_factor, Tlj) return self.xs_factor * 10 * np.sum(np.absolute(Tmmp) ** 2, axis=(0, 1)) diff --git a/tests/test_wavefunction_continuity.py b/tests/test_wavefunction_continuity.py new file mode 100644 index 00000000..39281931 --- /dev/null +++ b/tests/test_wavefunction_continuity.py @@ -0,0 +1,94 @@ +"""The interior and exterior wavefunctions must agree at the channel radius. + +This pins the Bloch-surface source used to build the interior expansion +coefficients. It was previously wrong for coupled channels: the outgoing term +contracted the S-matrix with the wrong index and dropped the incoming weights, +so multichannel ``wavefunction=True`` solves were off by O(100%). +""" + +import numpy as np +import pytest + +from jitr import reactions, rmatrix +from jitr.reactions.wavefunction import Wavefunctions +from jitr.utils.kinematics import classical_kinematics + +A = 5 * np.pi +NBASIS = 30 +L = 1 + + +def _channels(nch, k=None, mu=None, eta=None): + system = reactions.ProjectileTargetSystem( + channel_radius=A, + lmax=L, + mass_target=44657.0, + mass_projectile=938.3, + Ztarget=20, + Zproj=1, + coupling=lambda l: np.eye(nch), + ) + if k is None: + return system.get_partial_wave_channels( + *classical_kinematics( + system.mass_target, system.mass_projectile, 42.1, 20.0 + ) + ) + return system.get_partial_wave_channels(0.0, 0.0, mu, k, eta) + + +def _potential(solver, channels, nch): + r = solver.radial_grid(channels.a, channels.k[0]) + V = np.zeros((nch, nch, NBASIS), dtype=np.complex128) + diagonal = (-40.0 - 3.0j) * np.exp(-r / 4) + for i in range(nch): + V[i, i] = diagonal * (1 + 0.1 * i) + for i in range(nch - 1): + V[i, i + 1] = V[i + 1, i] = -5.0 * np.exp(-r / 4) + return V + + +@pytest.mark.parametrize( + "nch,weights", + [ + (1, [1.0]), + (3, [1.0, 0.0, 0.0]), + (3, [0.0, 1.0, 0.0]), + (3, [0.6, 0.8, 0.0]), + ], +) +def test_interior_matches_exterior_at_boundary(nch, weights): + solver = rmatrix.Solver(NBASIS) + channels, asymptotics = _channels(nch) + ch, asym = channels[L], asymptotics[L] + weights = np.array(weights) + + _, S, coeffs, uext_prime = solver.solve( + ch, + asym, + local_potential=_potential(solver, ch, nch), + weights=weights, + wavefunction=True, + ) + wavefunctions = Wavefunctions(solver, coeffs, S, uext_prime, ch, weights) + u_interior = np.array([u(ch.a) for u in wavefunctions.uint()]) + u_exterior = np.array([u(ch.a)[0] for u in wavefunctions.uext()]) + np.testing.assert_allclose(u_interior, u_exterior, atol=1e-10) + + +def test_interior_matches_exterior_with_different_k_per_channel(): + # proton-like and neutron-like channels, as in Lane (p,n) + solver = rmatrix.Solver(NBASIS) + k = np.array([1.27, 1.13]) + mu = np.array([918.96, 920.21]) + eta = np.array([0.5, 0.0]) + channels, asymptotics = _channels(2, k, mu, eta) + ch, asym = channels[L], asymptotics[L] + + _, S, coeffs, uext_prime = solver.solve( + ch, asym, local_potential=_potential(solver, ch, 2), wavefunction=True + ) + wavefunctions = Wavefunctions(solver, coeffs, S, uext_prime, ch) + u_interior = np.array([u(ch.a) for u in wavefunctions.uint()]) + u_exterior = np.array([u(ch.a)[0] for u in wavefunctions.uext()]) + np.testing.assert_allclose(u_interior, u_exterior, atol=1e-10) From 2b024aafec6db8bc2806a74a1a1537330db57ef5 Mon Sep 17 00:00:00 2001 From: beykyle Date: Mon, 21 Sep 2026 21:41:13 -0400 Subject: [PATCH 07/11] add Frescox (p,n) IAS regression cases F9 and F10 48Ca(p,n)48Sc(IAS) DWBA at Elab(p) = 25 and 35 MeV, from decks contributed by Jin Lei (Tongji University) and re-run locally with Frescox 7.2-20-ga7f491. These are the first non-elastic regression cases and the first external check on the spin-flip terms of the (p,n) amplitude: Frescox does its own channel-spin algebra, so it is independent of jitR and CHEX. jitR reproduces both to better than 1.3e-4 at every angle; with the pre-fix Clebsch-Gordan argument order both cases fail. Harness: build_case dispatches on observable_type, BuiltCase grows a dsdo extractor (the (p,n) workspace returns a bare array, elastic an ElasticXS), and _build_quasielastic_pn_case builds both channels from the deck's own lab energies. U1_central is left to jitR's default isovector difference, which equals the deck's tabulated form factor to 6e-9; U1_spin_orbit is zero because a Frescox KIND=1 form factor is central only. tools/make_pn_formfactor.py regenerates the form factor from the KD02 parameters, and documents two Frescox conventions: the header must carry explicit LOP/DER or the form factor is dropped silently and the cross section comes out identically zero, and FSCALE = sqrt(2)*sqrt(4 pi) cancels against Frescox's own KIND=1 coupling coefficient rather than being a free normalization. --- docs/regression-tests.md | 39 +- tests/regression/README.md | 9 +- tests/regression/_builders.py | 174 ++- tests/regression/frescox/README.md | 66 +- .../inputs/Ca48_pn_IAS_25MeV.formfactor | 1002 +++++++++++++ .../frescox/inputs/Ca48_pn_IAS_25MeV.in | 21 + .../inputs/Ca48_pn_IAS_35MeV.formfactor | 1002 +++++++++++++ .../frescox/inputs/Ca48_pn_IAS_35MeV.in | 21 + .../inputs/Ca48_pn_IAS_KD02_parameters.txt | 25 + .../frescox/outputs/Ca48_pn_IAS_25MeV.out | 1300 +++++++++++++++++ .../frescox/outputs/Ca48_pn_IAS_35MeV.out | 1300 +++++++++++++++++ .../reference/F10_p_ca48_pn_ias_35MeV.csv | 185 +++ .../reference/F10_p_ca48_pn_ias_35MeV.json | 109 ++ .../reference/F9_p_ca48_pn_ias_25MeV.csv | 185 +++ .../reference/F9_p_ca48_pn_ias_25MeV.json | 109 ++ .../frescox/tools/make_pn_formfactor.py | 115 ++ tests/regression/manifest.json | 12 + tests/regression/test_regression.py | 3 +- 18 files changed, 5653 insertions(+), 24 deletions(-) create mode 100644 tests/regression/frescox/inputs/Ca48_pn_IAS_25MeV.formfactor create mode 100644 tests/regression/frescox/inputs/Ca48_pn_IAS_25MeV.in create mode 100644 tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.formfactor create mode 100644 tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.in create mode 100644 tests/regression/frescox/inputs/Ca48_pn_IAS_KD02_parameters.txt create mode 100644 tests/regression/frescox/outputs/Ca48_pn_IAS_25MeV.out create mode 100644 tests/regression/frescox/outputs/Ca48_pn_IAS_35MeV.out create mode 100644 tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.csv create mode 100644 tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.json create mode 100644 tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.csv create mode 100644 tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.json create mode 100644 tests/regression/frescox/tools/make_pn_formfactor.py diff --git a/docs/regression-tests.md b/docs/regression-tests.md index e296f112..1225bb3c 100644 --- a/docs/regression-tests.md +++ b/docs/regression-tests.md @@ -9,21 +9,30 @@ TALYS. Reference CSVs are committed; neither external code is required in CI. uv run pytest tests/regression/ ``` -## Frescox cases (F1–F8) - -Eight elastic-scattering cases against LLNL -[Frescox](https://github.com/LLNL/Frescox), all for p/n + `78Ni`: - -| Case | Projectile | E\_lab (MeV) | Source deck | -|------|-----------|-------------|-------------| -| F1 | p | 6.9 | `B1-example-el.out` (block 1) | -| F2 | p | 11.0 | `B1-example-el.out` (block 2) | -| F3 | p | 49.35 | `B1-example-el.out` (block 3) | -| F4 | p | 100 | `B1-high-el.in` (block 1) | -| F5 | p | 200 | `B1-high-el.in` (block 2) | -| F6 | n | 49.35 | `B1_n-high-el.in` (block 1) | -| F7 | n | 100 | `B1_n-high-el.in` (block 2) | -| F8 | n | 200 | `B1_n-high-el.in` (block 3) | +## Frescox cases (F1–F10) + +Ten cases against LLNL [Frescox](https://github.com/LLNL/Frescox): eight +elastic for p/n + `78Ni`, and two quasi-elastic `(p,n)` to the isobaric +analog state of `48Ca`. + +| Case | Reaction | Projectile | E\_lab (MeV) | Source deck | +|------|----------|-----------|-------------|-------------| +| F1 | elastic | p | 6.9 | `B1-example-el.out` (block 1) | +| F2 | elastic | p | 11.0 | `B1-example-el.out` (block 2) | +| F3 | elastic | p | 49.35 | `B1-example-el.out` (block 3) | +| F4 | elastic | p | 100 | `B1-high-el.in` (block 1) | +| F5 | elastic | p | 200 | `B1-high-el.in` (block 2) | +| F6 | elastic | n | 49.35 | `B1_n-high-el.in` (block 1) | +| F7 | elastic | n | 100 | `B1_n-high-el.in` (block 2) | +| F8 | elastic | n | 200 | `B1_n-high-el.in` (block 3) | +| F9 | `48Ca(p,n)48Sc(IAS)` | p | 25 | `Ca48_pn_IAS_25MeV.in` | +| F10 | `48Ca(p,n)48Sc(IAS)` | p | 35 | `Ca48_pn_IAS_35MeV.in` | + +F9 and F10 exercise `jitr.xs.quasielastic_pn` rather than +`jitr.xs.elastic`. Frescox does its own channel-spin algebra, so they are an +independent check on the spin-flip terms of the `(p,n)` amplitude, which the +elastic cases cannot see. jitR reproduces both to better than 1.3e-4 at every +angle. ## TALYS cases (T1–T4) diff --git a/tests/regression/README.md b/tests/regression/README.md index b26e3f72..a1aaa8c2 100644 --- a/tests/regression/README.md +++ b/tests/regression/README.md @@ -1,7 +1,7 @@ # Regression harness End-to-end tests comparing `jitr` against committed reference outputs from -Frescox (F1–F8) and TALYS (T1–T4). See the +Frescox (F1–F10) and TALYS (T1–T4). See the [regression-tests documentation](../../docs/regression-tests.md) for a case overview. @@ -24,3 +24,10 @@ overview. kinematics, matching the upstream deck convention exactly. - Neutral elastic cases omit the Coulomb matrix in the builder; neutron metadata stays physically literal. +- The `(p,n)` cases (F9, F10) use `observable_type: "quasielastic_pn"` and + build `jitr.xs.quasielastic_pn.Workspace`. Both channels take their lab + energy straight from the deck, so jitR and Frescox see identical + kinematics rather than jitR's Q-value tables. `U1_central` is left to + jitR's default isovector difference, which is what the deck's tabulated + form factor holds; `U1_spin_orbit` is zero because a Frescox `KIND=1` form + factor is central only. diff --git a/tests/regression/_builders.py b/tests/regression/_builders.py index efe816af..59bb1c3e 100644 --- a/tests/regression/_builders.py +++ b/tests/regression/_builders.py @@ -1,6 +1,7 @@ from __future__ import annotations -from dataclasses import dataclass +from collections.abc import Callable +from dataclasses import dataclass, field from typing import Any import numpy as np @@ -17,12 +18,13 @@ spin_orbit_jlmb, ) from jitr.optical_potentials.omp import LocalOpticalPotential -from jitr.reactions import ElasticReaction, Nucleus, Particle +from jitr.reactions import ElasticReaction, Nucleus, Particle, Reaction from jitr.rmatrix import Solver from jitr.utils.constants import AMU from jitr.utils.density import density_table from jitr.utils.kinematics import classical_kinematics, classical_kinematics_cm from jitr.xs.elastic import DifferentialWorkspace +from jitr.xs.quasielastic_pn import Workspace as QuasielasticPnWorkspace from ._readers import ReferenceCase @@ -33,15 +35,177 @@ class BuiltCase: workspace: Any xs_kwargs: dict[str, np.ndarray | None] + # elastic workspaces return an ElasticXS; the (p,n) workspace returns dsdo itself + extract_dsdo: Callable[[Any], np.ndarray] = field( + default=lambda result: result.dsdo + ) + + def dsdo(self) -> np.ndarray: + """Return the differential cross section in mb/sr for this case.""" + return self.extract_dsdo(self.workspace.xs(**self.xs_kwargs)) def build_case(ref: ReferenceCase) -> BuiltCase: """Build the workspace and input arrays for a committed reference case.""" - if ref.observable_type != "elastic": + if ref.observable_type == "elastic": + return _build_elastic_case(ref) + if ref.observable_type == "quasielastic_pn": + return _build_quasielastic_pn_case(ref) + raise NotImplementedError( + f"{ref.case_id} uses unsupported observable_type {ref.observable_type!r}" + ) + + +def _evaluate_local_potential( + reaction_model, + channel_kinematics, + radial_grid: np.ndarray, + block: dict[str, Any], + coulomb_radius: float, + scale_radii_by_At_and_Ap: bool, +) -> tuple[np.ndarray, np.ndarray, Any]: + """Evaluate one KD-style local potential block on the quadrature grid. + + ``block`` carries the 13 KD02 parameters as named in the deck + (``V rv av W rw aw Wd rvd avd Vso Wso rvso avso``); the surface real depth + ``Vd`` is zero for these decks. + """ + model = LocalOpticalPotential( + scale_radii_by_At_and_Ap=scale_radii_by_At_and_Ap, + ) + return model.evaluate( + radial_grid, + reaction_model, + channel_kinematics, + float(block["V"]), + float(block["rv"]), + float(block["av"]), + float(block["W"]), + float(block["rw"]), + float(block["aw"]), + float(block["Wd"]), + 0.0, + float(block["rvd"]), + float(block["avd"]), + float(block["Vso"]), + float(block["Wso"]), + float(block["rvso"]), + float(block["avso"]), + coulomb_radius, + ) + + +def _build_quasielastic_pn_case(ref: ReferenceCase) -> BuiltCase: + """Build a DWBA (p,n) case against a Frescox charge-exchange deck. + + Both channels use the deck's own lab energies and integer-amu masses, so + jitR and Frescox see identical kinematics. ``U1_central`` is left to jitR's + default isovector difference, which equals the form factor the deck reads; + ``U1_spin_orbit`` is zero because a Frescox ``KIND=1`` form factor is + central only. + """ + metadata = ref.metadata + reaction_data = metadata["reaction"] + mass_kwargs = metadata.get("mass_kwargs", {}) + mass_model = metadata.get("mass_model", "tabulated") + particles = { + name: _build_reaction_particle(reaction_data[name], mass_model, mass_kwargs) + for name in ("target", "projectile", "product", "residual") + } + reaction = Reaction( + particles["target"], + particles["projectile"], + particles["product"], + particles["residual"], + mass_kwargs=mass_kwargs, + ) + exit_reaction = Reaction( + particles["residual"], + particles["product"], + process="El", + mass_kwargs=mass_kwargs, + ) + + kinematics = metadata["kinematics"] + frame = kinematics["frame"] + if frame != "lab" or bool(kinematics.get("relativistic", True)): + raise NotImplementedError( + f"{ref.case_id}: (p,n) cases expect non-relativistic lab kinematics" + ) + kinematics_entrance = classical_kinematics( + reaction.target.m0, + reaction.projectile.m0, + float(kinematics["energy_MeV"]), + reaction.target.Z * reaction.projectile.Z, + ) + kinematics_exit = classical_kinematics( + exit_reaction.target.m0, + exit_reaction.projectile.m0, + float(kinematics["exit_energy_MeV"]), + exit_reaction.target.Z * exit_reaction.projectile.Z, + ) + + matching = metadata["matching"] + workspace = QuasielasticPnWorkspace( + reaction=reaction, + kinematics_entrance=kinematics_entrance, + kinematics_exit=kinematics_exit, + solver=Solver(int(matching["nbasis"])), + angles=ref.theta_cm_rad, + lmax=int(matching["lmax"]), + channel_radius_fm=float(matching["channel_radius_fm"]), + tmatrix_abs_tol=0.0, + ) + + potential = metadata["optical_potential"] + kind = potential["kind"] + if kind != "woods_saxon_local_pn": + raise NotImplementedError( + f"{ref.case_id} uses unsupported optical_potential.kind {kind!r}" + ) + scale_radii = bool(potential["scale_radii_by_At_and_Ap"]) + radial_grid = workspace.radial_grid() + coulomb_radius = float(potential["coulomb"]["rC"]) + proton_central, proton_spin_orbit, proton_coulomb = _evaluate_local_potential( + reaction, + kinematics_entrance, + radial_grid, + potential["proton"], + coulomb_radius, + scale_radii, + ) + neutron_central, neutron_spin_orbit, _ = _evaluate_local_potential( + exit_reaction, + kinematics_exit, + radial_grid, + potential["neutron"], + coulomb_radius, + scale_radii, + ) + + transition = metadata["transition_potential"] + if transition["central"] != "default_isovector_difference": raise NotImplementedError( - f"{ref.case_id} uses unsupported observable_type {ref.observable_type!r}" + f"{ref.case_id}: unsupported transition_potential.central " + f"{transition['central']!r}" ) - return _build_elastic_case(ref) + if transition["spin_orbit"] != "zero": + raise NotImplementedError( + f"{ref.case_id}: unsupported transition_potential.spin_orbit " + f"{transition['spin_orbit']!r}" + ) + return BuiltCase( + workspace=workspace, + xs_kwargs={ + "U_p_coulomb": np.asarray(proton_coulomb, dtype=np.complex128), + "U_p_central": np.asarray(proton_central, dtype=np.complex128), + "U_p_spin_orbit": np.asarray(proton_spin_orbit, dtype=np.complex128), + "U_n_central": np.asarray(neutron_central, dtype=np.complex128), + "U_n_spin_orbit": np.asarray(neutron_spin_orbit, dtype=np.complex128), + "U1_spin_orbit": np.zeros_like(radial_grid, dtype=np.complex128), + }, + extract_dsdo=lambda result: np.asarray(result, dtype=np.float64), + ) def _build_jlm_elastic_case( diff --git a/tests/regression/frescox/README.md b/tests/regression/frescox/README.md index c200e074..411227c7 100644 --- a/tests/regression/frescox/README.md +++ b/tests/regression/frescox/README.md @@ -2,7 +2,8 @@ Cases F1–F8 are adapted from the upstream `B1-example-el` example published at (F1–F3) and repo-local high-energy decks -(F4–F8). +(F4–F8). Cases F9–F10 are quasi-elastic `48Ca(p,n)48Sc(IAS)` DWBA decks +contributed by Jin Lei (Tongji University), re-run locally. ## Building Frescox locally @@ -55,9 +56,72 @@ uv run python tests/regression/frescox/tools/parse_frescox.py \ # repeat for F5 (--case-index 1), F6/F7/F8 from B1_n-high-el.out ``` +## Regenerating the (p,n) IAS cases (F9–F10) + +The deck reads its transition potential from `fort.4` in the working +directory. Write it from the KD02 parameters in the case metadata, run the +deck, and parse the outgoing-neutron block: + +```bash +FRESCOX=/tmp/jitr-frescox/Frescox/source/frescox + +for E in 25 35; do + CASE=$([ "$E" = 25 ] && echo F9 || echo F10)_p_ca48_pn_ias_${E}MeV + + uv run python tests/regression/frescox/tools/make_pn_formfactor.py \ + --metadata tests/regression/frescox/reference/$CASE.json \ + --out tests/regression/frescox/inputs/Ca48_pn_IAS_${E}MeV.formfactor + + workdir=$(mktemp -d) + cp tests/regression/frescox/inputs/Ca48_pn_IAS_${E}MeV.formfactor $workdir/fort.4 + (cd $workdir && $FRESCOX) \ + < tests/regression/frescox/inputs/Ca48_pn_IAS_${E}MeV.in \ + > tests/regression/frescox/outputs/Ca48_pn_IAS_${E}MeV.out + + uv run python tests/regression/frescox/tools/parse_frescox.py \ + --output tests/regression/frescox/outputs/Ca48_pn_IAS_${E}MeV.out \ + --metadata tests/regression/frescox/reference/$CASE.json \ + --csv-out tests/regression/frescox/reference/$CASE.csv \ + --case-index 1 --min-angle-deg 1.0 +done +``` + +`--case-index 1` selects the outgoing-neutron partition; block 0 is proton +elastic. + +### Two Frescox conventions worth knowing + +**A malformed form-factor header fails silently.** Frescox first reads the +`fort.4` header expecting trailing `LOP` and `DER` integers, and only falls +back to the shorter historical header on an I/O error. That fallback re-reads +after the failed record, swallowing the first data line, and the form factor +is then dropped with no diagnostic: the `(p,n)` cross section comes out +identically zero. `make_pn_formfactor.py` always writes `LOP = DER = -1` +explicitly. If a charge-exchange deck returns exactly zero at every angle, +suspect this first. + +**`FSCALE = sqrt(2) * sqrt(4 pi)` is not a fudge factor.** For a local +`KIND=1` form factor with `IP3=0`, `INTER` scales the table by +`ASCALE = FSCALE * R4PI` with `R4PI = 1/sqrt(4 pi)` (`frxx7a.f`, `globx7.f`), +so the `sqrt(4 pi)` cancels `R4PI`. The remaining `sqrt(2)` is +`sqrt(2 j_p + 1)` for the spin-1/2 projectile: Frescox reads the table as a +reduced matrix element, and the coupling coefficient it multiplies +(`frxx4.f`, `IP3=0` branch) evaluates to exactly `1/sqrt(2)` for every +`(l, j)` in this deck. The two cancel, so Frescox's matrix element is the +plain `U1` and jitR applies no such factor. This was verified two ways: by +evaluating that coupling coefficient symbolically, and by jitR reproducing +the Frescox cross section absolutely. + ## Notes - Frescox's `elab`/`nlab` NAMELIST supports at most four energies per deck, so the high-energy proton and neutron ladders are split into separate `B1-high-el` and `B1_n-high-el` input decks. - B2 and B5 are not landed yet (requires a public `jitr.xs.dwba` workspace). +- F9/F10 guard the spin-flip terms of the `(p,n)` amplitude: with the + Clebsch-Gordan arguments in their pre-fix order, jitR falls below the + Frescox reference by up to a factor 15 at 25 MeV and 84 at 35 MeV in the + backward hemisphere, and both cases fail. +- Our run of the F9/F10 decks reproduces the angular distributions supplied + with them (`fresco_dsdo.dat`, FRES 3.4 on macOS/ARM) to a ratio of + 1.000000 at every angle, so the two Frescox lineages agree exactly here. diff --git a/tests/regression/frescox/inputs/Ca48_pn_IAS_25MeV.formfactor 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zt=21 qval=-0.5 nex=1 / + &STATES jp=0.5 bandp=1 ep=0 cpot=2 jt=0 bandt=1 et=6.677 / + &partition / + &POT kp=1 type=0 p1=48 p2=0 p3=1.27126845 / + &POT kp=1 type=1 p1=50.96318785 p2=1.19234989 p3=0.67066240 p4=2.29860499 p5=1.19234989 p6=0.67066240 / + &POT kp=1 type=2 p4=8.13772168 p5=1.28479728 p6=0.54368400 / + &POT kp=1 type=3 p1=5.32128688 p2=1.00737109 p3=0.59000000 p4=-0.12462904 p5=1.00737109 p6=0.59000000 / + &POT kp=2 type=0 p1=48 p2=0 p3=1.00000000 / + &POT kp=2 type=1 p1=45.32118466 p2=1.19234989 p3=0.67066240 p4=1.47531087 p5=1.19234989 p6=0.67066240 / + &POT kp=2 type=2 p4=6.52925801 p5=1.28479728 p6=0.53665120 / + &POT kp=2 type=3 p1=5.43011446 p2=1.00737109 p3=0.59000000 p4=-0.09011411 p5=1.00737109 p6=0.59000000 / + &pot / + &overlap / + &COUPLING icto=2 icfrom=1 kind=1 ip1=0 ip2=2 ip3=0 p1=1.0 p2=1.0 / + &coupling / diff --git a/tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.formfactor b/tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.formfactor new file mode 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-4.0783481612e-11 +-1.7683857874e-10 -3.9563804244e-11 +-1.7164288505e-10 -3.8380720287e-11 +-1.6659984601e-10 -3.7233127775e-11 +-1.6170497648e-10 -3.6119958071e-11 +-1.5695392311e-10 -3.5040174847e-11 +-1.5234246041e-10 -3.3992773105e-11 +-1.4786648708e-10 -3.2976778227e-11 diff --git a/tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.in b/tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.in new file mode 100644 index 00000000..9faeceb5 --- /dev/null +++ b/tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.in @@ -0,0 +1,21 @@ +48Ca(p,n)48Sc(IAS) DWBA +NAMELIST + &FRESCO hcm=0.02 rmatch=20.0 jtmin=0 jtmax=40 absend=-1 thmin=0 thmax=180 thinc=1 + chans=1 smats=2 xstabl=3 kqmax=1 pp=0 koords=0 elab(1)=35.0 iter=1 / + &PARTITION namep='p' massp=1.0 zp=1 namet='48Ca' masst=48.0 zt=20 qval=0 nex=1 / + &STATES jp=0.5 bandp=1 ep=0 cpot=1 jt=0 bandt=1 et=0 / + &PARTITION namep='n' massp=1.0 zp=0 namet='48Sc' masst=48.0 zt=21 qval=-0.5 nex=1 / + &STATES jp=0.5 bandp=1 ep=0 cpot=2 jt=0 bandt=1 et=6.677 / + &partition / + &POT kp=1 type=0 p1=48 p2=0 p3=1.27126845 / + &POT kp=1 type=1 p1=47.13547773 p2=1.19234989 p3=0.67066240 p4=3.53838802 p5=1.19234989 p6=0.67066240 / + &POT kp=1 type=2 p4=6.85454469 p5=1.28479728 p6=0.54368400 / + &POT kp=1 type=3 p1=5.11263624 p2=1.00737109 p3=0.59000000 p4=-0.20652470 p5=1.00737109 p6=0.59000000 / + &POT kp=2 type=0 p1=48 p2=0 p3=1.00000000 / + &POT kp=2 type=1 p1=42.02375462 p2=1.19234989 p3=0.67066240 p4=2.49229500 p5=1.19234989 p6=0.67066240 / + &POT kp=2 type=2 p4=5.63171244 p5=1.28479728 p6=0.53665120 / + &POT kp=2 type=3 p1=5.21719662 p2=1.00737109 p3=0.59000000 p4=-0.16293202 p5=1.00737109 p6=0.59000000 / + &pot / + &overlap / + &COUPLING icto=2 icfrom=1 kind=1 ip1=0 ip2=2 ip3=0 p1=1.0 p2=1.0 / + &coupling / diff --git a/tests/regression/frescox/inputs/Ca48_pn_IAS_KD02_parameters.txt b/tests/regression/frescox/inputs/Ca48_pn_IAS_KD02_parameters.txt new file mode 100644 index 00000000..6d76ecdb --- /dev/null +++ b/tests/regression/frescox/inputs/Ca48_pn_IAS_KD02_parameters.txt @@ -0,0 +1,25 @@ +Configuration of the two FRESCO decks in this bundle. +Constants are FRESCO's own (frxx0.f): hbar*c = 197.32705 MeV fm, amu = 931.49432 MeV, +1/alpha = 137.03599; nonrelativistic kinematics; masses taken as exactly 1 and 48 amu. +KD02 = Koning and Delaroche, Nucl. Phys. A713 (2003) 231, local form, 13 parameters, +Thomas spin-orbit with (hbar/m_pi c)^2 = 2.0 fm^2. + +=== Elab(p) = 25.0 MeV === + Ecm(in) = 24.48979592 MeV Q = -0.5 MeV E_IAS = 6.677 MeV + Ecm(out) = 17.31279592 MeV Elab(n) = 17.67347917 MeV + k_p = 1.07135559 fm^-1 eta_p = 0.62994184 k_n = 0.90079230 fm^-1 eta_n = 0 + KD02 p + (A=48,Z=20) at 25.0 MeV, rc = 1.27126845 fm: + V=50.96318785 rv=1.19234989 av=0.67066240 W=2.29860499 rw=1.19234989 aw=0.67066240 Wd=8.13772168 rvd=1.28479728 avd=0.54368400 Vso=5.32128688 Wso=-0.12462904 rvso=1.00737109 avso=0.59000000 + KD02 n + (A=48,Z=21) at 17.673479 MeV: + V=45.32118466 rv=1.19234989 av=0.67066240 W=1.47531087 rw=1.19234989 aw=0.67066240 Wd=6.52925801 rvd=1.28479728 avd=0.53665120 Vso=5.43011446 Wso=-0.09011411 rvso=1.00737109 avso=0.59000000 + nangles = 181 theta_cm 0 to 180 step 1 deg + +=== Elab(p) = 35.0 MeV === + Ecm(in) = 34.28571429 MeV Q = -0.5 MeV E_IAS = 6.677 MeV + Ecm(out) = 27.10871429 MeV Elab(n) = 27.67347917 MeV + k_p = 1.26764503 fm^-1 eta_p = 0.53239803 k_n = 1.12718583 fm^-1 eta_n = 0 + KD02 p + (A=48,Z=20) at 35.0 MeV, rc = 1.27126845 fm: + V=47.13547773 rv=1.19234989 av=0.67066240 W=3.53838802 rw=1.19234989 aw=0.67066240 Wd=6.85454469 rvd=1.28479728 avd=0.54368400 Vso=5.11263624 Wso=-0.20652470 rvso=1.00737109 avso=0.59000000 + KD02 n + (A=48,Z=21) at 27.673479 MeV: + V=42.02375462 rv=1.19234989 av=0.67066240 W=2.49229500 rw=1.19234989 aw=0.67066240 Wd=5.63171244 rvd=1.28479728 avd=0.53665120 Vso=5.21719662 Wso=-0.16293202 rvso=1.00737109 avso=0.59000000 + nangles = 181 theta_cm 0 to 180 step 1 deg diff --git a/tests/regression/frescox/outputs/Ca48_pn_IAS_25MeV.out b/tests/regression/frescox/outputs/Ca48_pn_IAS_25MeV.out new file mode 100644 index 00000000..49f75e7e --- /dev/null +++ b/tests/regression/frescox/outputs/Ca48_pn_IAS_25MeV.out @@ -0,0 +1,1300 @@ +Running on kyle-ThinkPad-X390 + FRESCOX - version 7.2-20-ga7f491: Coupled Reaction Channels on gfortran + + Using NAMELIST input + + 48Ca(p,n)48Sc(IAS) DWBA + + 0.020 20.000 0.500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + + Centre-of-mass Range is 1002 * 0.0200 fm., Maximum at 20.00 fm., Interpolating NL forms every 0.50 fm. + Non - locality width is 4 * 0.0200 fm., Maximum of 0.06 fm., Centred at 0.00 fm. + 2-Nucleon Separation of 0 * 0.5000 fm., Maximum of 0.00 fm., Minimum at 0.00 fm. + Maximum single particle bins of 20.0000 fm. + M,Mint = 1001 1001 + + + Range of total J is 0.0 <= J <= 40.0 (at least 0.0) and Absorbtion => -1.0000 mb. + Dry Run = F, CC set limits = 0 0, Relativistic kinematics = , Both/Far/Near Analyses = 1 + + Cross Sections (and up to T1 for 0=projectile) for Theta from 0.0 to 180.0 in steps of 1.0 degrees, DGAM=0, grace=T, Coordinates = 0 (Mads) + + Lower Radial Cutoff = maximum of -1.60*L*h & 0.0 fm., Lower Cutoff for Couplings = 0.0 fm. + + + Iterate Couplings between 0 and 1 times, to 0.000 % if sooner. + Block solved exactly = 0 chs., with Pade = 0 & Isocen = =0, NOSOL = F, CCREAL = F, initwf = 0 + Small channels are 0.00E+00 and small couplings are 1.00E-12 of unitarity + + NL quadrature with 18 Gaussian points, Calculate multipoles up to 50 from 0 + M-transfers for lp+lt greater than or equal to 6, Angular Integration Cutoff below 2.7778 % + + + Trace switches are : CHANS = 1, LISTCC = 0, TRENEG = 0, CDETR = 0, SMATS = 2, XSTABL = 3, NLPL = 0 + + WAVES = 0, LAMPL = 0, VEFF = 0, KFUS = 0, WDISK = 0, BPM = 0, MELFIL = 0 + + CDCC = 0, NFUS = 0, TCFILE = 0 + + Using unit mass = 1.000000 amu and 1/fine-structure constant = 137.03599 ( 1.000000 * true ) with hc = 197.32705 MeV.fm, + thus 2*amu/hbar^2 = 0.0478450 = 1/20.9008 and Coulomb constant = 0.1574855 so e^2 = 1.43996515, and nuclear magneton= 0.1261183 + + Now pre-scan input and save to file 3 + + + + *********** PARTITION NUMBER 1 ****************************************************************************************** + + PROJ=p MASS= 1.0000 Z= 1.0, # STATES= 1T, TARG=48Ca MASS= 48.0000 Z= 20.0, Q-VALUE = 0.0000 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 0.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 1 J= 0.0+ (B# 1), E= 0.0000, K= 0.0 + + + *********** PARTITION NUMBER 2 ****************************************************************************************** + + PROJ=n MASS= 1.0000 Z= 0.0, # STATES= 1T, TARG=48Sc MASS= 48.0000 Z= 21.0, Q-VALUE = -0.5000 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 0.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 2 J= 0.0+ (B# 1), E= 6.6770, K= 0.0 + + + ************************************************************************************************************************************ + + The following POTENTIALS are defined : + + KP# TYPE IT SHAPE at V1 r1 a1 V2 r2 a2 A-in A-used + + + A#1 A#2 r0c ac h @ 1 + 1 0=Coulomb 0=CHARGE (WS) 1 48.000 0.000 1.2713 0.0000 0.0000 0.0000 0.000 48.000 0.02000 + + 1 1=Volume 0=Woods-Saxon 2 50.9632 1.1923 0.6707 2.2986 1.1923 0.6707 0.000 48.000 + + 1 2=Surface 0=Woods-Saxon 3 0.0000 0.0000 0.0000 8.1377 1.2848 0.5437 0.000 48.000 + + 1 3=Projtl S.O. 0=Woods-Saxon 4 5.3213 1.0074 0.5900 -0.1246 1.0074 0.5900 0.000 48.000 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 2 + 2 0=Coulomb 0=CHARGE (WS) 5 48.000 0.000 1.0000 0.0000 0.0000 0.0000 0.000 48.000 0.02000 + + 2 1=Volume 0=Woods-Saxon 6 45.3212 1.1923 0.6707 1.4753 1.1923 0.6707 0.000 48.000 + + 2 2=Surface 0=Woods-Saxon 7 0.0000 0.0000 0.0000 6.5293 1.2848 0.5367 0.000 48.000 + + 2 3=Projtl S.O. 0=Woods-Saxon 8 5.4301 1.0074 0.5900 -0.0901 1.0074 0.5900 0.000 48.000 + + ************************************************************************************************************************************ + + TWO-way COUPLING # 1 for partitions 2 <- 1 of KIND 1, 0 2 0 -1 -1 & P1,P2 = 1.0000 1.0000 : for J <= 40.5 & R < 19.9 fm. + + General projectile/target multipole+spin transfers + Therefore from file 4 read LOCAL form factor of COMPLEX elements, and use as given. with Re,Im scalings of 1.0000 1.0000 + + Read 1001 point form factor at h = 0.020 fm from 0.000:Lane U1 central + Scaled by 5.0133 for L-transfer 0, projectile transfer 0.0, and target transfer = 0.0 to excited pair 1 from pair 1 + Angular momentum operator itself: -1 on wf derivative: -1 + + + Incoming partition 1 in excitation state # 1, Laboratory Energy given for partition 1 Nucleus 1 in Excitation pair 1 + +0Lab. ENERGY ranges : + + from 25.0000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + + Largest real,imaginary parts of any form factor at R= 19.80 are 7.27E-02 2.22E-10 MeV + Finished all Couplings @ 1.78499997E-03 + + Symmetric Hamiltonian +1*********************************************************************************************************************************** +************************************************************************************************************************************ + + INCOMING p ; LABORATORY p ENERGY = 25.000 MeV. + + *********************************************************************************************************************************** + *********************************************************************************************************************************** + + Allocate arrays for 2 channels, of which 1 need wfs. + + ######################################################################################################################### + # # + # Total SPIN and PARITY = 0.5 +, 2 channels, 0 in 1st block. Rmin & Coul turning = 0.0 1.176E+00 fm. # + # # + ######################################################################################################################### + + + + C Projectl Target # EX. (L Proj) J + Targ = Jtotal E-cm Re K Re Eta RM*K CH G-REL + 1 p 48Ca # 1: I 0 0.5 0.5 0.0 0.5 24.48980 1.07136 0.62994 21.4271 0.03058 0.50670 1 1 1 1 1.00000 + 2 n 48Sc # 1: 0 0.5 0.5 0.0 0.5 17.31280 0.90079 0.00000 18.0158 0.37022 0.33607 2 1 2 1 1.00000 + S-matrix 1 = -0.34600 -0.15831 for L= 0, J= 0.5 channel on core I = 0.0 from L= 0, Acc. loss = 0.0 D. + Elastic phase shift 1 = -77.707 27.682 deg. for the L = 0, J = 0.5 channel. + 0.5 -0.34599992 -0.15831232i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 0.5+/ 0 @ 1 = 23.408 , Out: 0.000# 0.482 22.926f + + + Total SPIN, PARITY = 0.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.0 1.2 + S-matrix 1 = -0.09947 0.40648 for L= 1, J= 0.5 channel on core I = 0.0 from L= 1, Acc. loss = 0.0 D. + Elastic phase shift 1 = 51.875 24.956 deg. for the L = 1, J = 0.5 channel. + 0.5 -0.09946762 0.40648388i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 0.5-/ 1 @ 1 = 22.577 , Out: 0.000# 0.465 22.112f + + + Total SPIN, PARITY = 1.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.0 2.0 + S-matrix 1 = 0.18966 0.30866 for L= 2, J= 1.5 channel on core I = 0.0 from L= 2, Acc. loss = 0.0 D. + Elastic phase shift 1 = 29.216 29.088 deg. for the L = 2, J = 1.5 channel. + 1.5 0.18965749 0.30865859i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 1.5+/ 2 @ 1 = 47.557 , Out: 0.000# 0.807 46.749f + + + Total SPIN, PARITY = 1.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.0 2.0 + S-matrix 1 = -0.20940 0.35221 for L= 1, J= 1.5 channel on core I = 0.0 from L= 1, Acc. loss = 0.0 D. + Elastic phase shift 1 = 60.366 25.560 deg. for the L = 1, J = 1.5 channel. + 1.5 -0.20939834 0.35220812i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 1.5-/ 1 @ 1 = 45.550 , Out: 0.000# 0.945 44.605f + + + Total SPIN, PARITY = 2.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.1 2.9 + S-matrix 1 = 0.02835 0.41735 for L= 2, J= 2.5 channel on core I = 0.0 from L= 2, Acc. loss = 0.0 D. + Elastic phase shift 1 = 43.057 24.968 deg. for the L = 2, J = 2.5 channel. + 2.5 0.02834518 0.41734576i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 2.5+/ 2 @ 1 = 67.744 , Out: 0.000# 1.216 66.527f + + + Total SPIN, PARITY = 2.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.1 2.9 + S-matrix 1 = 0.26048 -0.22016 for L= 3, J= 2.5 channel on core I = 0.0 from L= 3, Acc. loss = 0.0 D. + Elastic phase shift 1 = -20.103 30.816 deg. for the L = 3, J = 2.5 channel. + 2.5 0.26048097 -0.22016391i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 2.5-/ 3 @ 1 = 72.560 , Out: 0.000# 1.103 71.458f + + + Total SPIN, PARITY = 3.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.1 3.9 + S-matrix 1 = 0.01231 -0.40302 for L= 4, J= 3.5 channel on core I = 0.0 from L= 4, Acc. loss = 0.0 D. + Elastic phase shift 1 = -44.125 26.021 deg. for the L = 4, J = 3.5 channel. + 3.5 0.01231208 -0.40302040i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 3.5+/ 4 @ 1 = 91.683 , Out: 0.000# 0.856 90.827f + + + Total SPIN, PARITY = 3.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.1 3.9 + S-matrix 1 = 0.26931 0.07535 for L= 3, J= 3.5 channel on core I = 0.0 from L= 3, Acc. loss = 0.0 D. + Elastic phase shift 1 = 7.815 36.503 deg. for the L = 3, J = 3.5 channel. + 3.5 0.26931162 0.07534966i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 3.5-/ 3 @ 1 = 100.920 , Out: 0.000# 1.491 99.429f + + + Total SPIN, PARITY = 4.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.1 4.8 + S-matrix 1 = 0.38231 -0.34286 for L= 4, J= 4.5 channel on core I = 0.0 from L= 4, Acc. loss = 0.0 D. + Elastic phase shift 1 = -20.943 19.092 deg. for the L = 4, J = 4.5 channel. + 4.5 0.38230951 -0.34285915i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 4.5+/ 4 @ 1 = 100.763 , Out: 0.000# 1.059 99.704f + + + Total SPIN, PARITY = 4.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.1 4.8 + S-matrix 1 = 0.21402 0.07693 for L= 5, J= 4.5 channel on core I = 0.0 from L= 5, Acc. loss = 0.0 D. + Elastic phase shift 1 = 9.886 42.425 deg. for the L = 5, J = 4.5 channel. + 4.5 0.21402048 0.07693213i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 4.5-/ 5 @ 1 = 129.774 , Out: 0.000# 0.534 129.240f + + + Total SPIN, PARITY = 5.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 5.7 + S-matrix 1 = 0.74665 0.17925 for L= 6, J= 5.5 channel on core I = 0.0 from L= 6, Acc. loss = 0.0 D. + Elastic phase shift 1 = 6.750 7.567 deg. for the L = 6, J = 5.5 channel. + 5.5 0.74664601 0.17925086i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 5.5+/ 6 @ 1 = 67.395 , Out: 0.000# 0.064 67.332f + + + Total SPIN, PARITY = 5.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 5.7 + S-matrix 1 = 0.20964 -0.23157 for L= 5, J= 5.5 channel on core I = 0.0 from L= 5, Acc. loss = 0.0 D. + Elastic phase shift 1 = -23.923 33.334 deg. for the L = 5, J = 5.5 channel. + 5.5 0.20963657 -0.23157157i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 5.5-/ 5 @ 1 = 148.199 , Out: 0.000# 1.018 147.181f + + + Total SPIN, PARITY = 6.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 6.7 + S-matrix 1 = 0.64513 0.20231 for L= 6, J= 6.5 channel on core I = 0.0 from L= 6, Acc. loss = 0.0 D. + Elastic phase shift 1 = 8.705 11.213 deg. for the L = 6, J = 6.5 channel. + 6.5 0.64512581 0.20230516i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 6.5+/ 6 @ 1 = 104.013 , Out: 0.000# 0.138 103.875f + + + Total SPIN, PARITY = 6.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 6.7 + S-matrix 1 = 0.94422 0.07224 for L= 7, J= 6.5 channel on core I = 0.0 from L= 7, Acc. loss = 0.0 D. + Elastic phase shift 1 = 2.188 1.561 deg. for the L = 7, J = 6.5 channel. + 6.5 0.94421630 0.07224251i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 6.5-/ 7 @ 1 = 19.779 , Out: 0.000# 3.921/ 19.776f + + + Total SPIN, PARITY = 7.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 7.6 + S-matrix 1 = 0.98788 0.02246 for L= 8, J= 7.5 channel on core I = 0.0 from L= 8, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.651 0.342 deg. for the L = 8, J = 7.5 channel. + 7.5 0.98788407 0.02245836i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 7.5+/ 8 @ 1 = 5.163 , Out: 0.000# 0.233/ 5.163f + + + Total SPIN, PARITY = 7.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 7.6 + S-matrix 1 = 0.93487 0.09026 for L= 7, J= 7.5 channel on core I = 0.0 from L= 7, Acc. loss = 0.0 D. + Elastic phase shift 1 = 2.757 1.797 deg. for the L = 7, J = 7.5 channel. + 7.5 0.93486823 0.09026216i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 7.5-/ 7 @ 1 = 25.810 , Out: 0.000# 5.656/ 25.805f + + + Total SPIN, PARITY = 8.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 8.5 + S-matrix 1 = 0.98721 0.02666 for L= 8, J= 8.5 channel on core I = 0.0 from L= 8, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.773 0.358 deg. for the L = 8, J = 8.5 channel. + 8.5 0.98720923 0.02665528i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 8.5+/ 8 @ 1 = 6.086 , Out: 0.000# 0.283/ 6.086f + + + Total SPIN, PARITY = 8.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 8.5 + S-matrix 1 = 0.99730 0.00660 for L= 9, J= 8.5 channel on core I = 0.0 from L= 9, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.190 0.077 deg. for the L = 9, J = 8.5 channel. + 8.5 0.99730028 0.00659809i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 8.5-/ 9 @ 1 = 1.318 , Out: 0.000# 0.014/ 1.318f + + + Total SPIN, PARITY = 9.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.3 9.5 + S-matrix 1 = 0.99939 0.00191 for L= 10, J= 9.5 channel on core I = 0.0 from L= 10, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.055 0.017 deg. for the L = 10, J = 9.5 channel. + 9.5 0.99938893 0.00190604i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 9.5+/10 @ 1 = 0.333 , Out: 0.000# 0.895# 0.333f + + + Total SPIN, PARITY = 9.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.3 9.5 + S-matrix 1 = 0.99726 0.00757 for L= 9, J= 9.5 channel on core I = 0.0 from L= 9, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.217 0.078 deg. for the L = 9, J = 9.5 channel. + 9.5 0.99725923 0.00756565i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 9.5-/ 9 @ 1 = 1.483 , Out: 0.000# 0.016/ 1.483f + + + Total SPIN, PARITY = 10.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.3 10.4 + S-matrix 1 = 0.99939 0.00214 for L= 10, J= 10.5 channel on core I = 0.0 from L= 10, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.061 0.017 deg. for the L = 10, J = 10.5 channel. + 10.5 0.99938897 0.00213615i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 10.5+/10 @ 1 = 0.366 , Out: 0.000# 0.993# 0.366f + + + Total SPIN, PARITY = 10.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.3 10.4 + S-matrix 1 = 0.99986 0.00055 for L= 11, J= 10.5 channel on core I = 0.0 from L= 11, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.016 0.004 deg. for the L = 11, J = 10.5 channel. + 10.5 0.99986026 0.00054650i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 10.5-/11 @ 1 = 0.084 , Out: 0.000# 0.057# 0.084f + + + Total SPIN, PARITY = 11.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.3 11.3 + S-matrix 1 = 0.99997 0.00016 for L= 12, J= 11.5 channel on core I = 0.0 from L= 12, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.004 0.001 deg. for the L = 12, J = 11.5 channel. + 11.5 0.99996775 0.00015595i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 11.5+/12 @ 1 = 0.021 , Out: 0.000# 0.004# 0.021f + + + Total SPIN, PARITY = 11.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.3 11.3 + S-matrix 1 = 0.99986 0.00060 for L= 11, J= 11.5 channel on core I = 0.0 from L= 11, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.017 0.004 deg. for the L = 11, J = 11.5 channel. + 11.5 0.99986112 0.00060213i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 11.5-/11 @ 1 = 0.091 , Out: 0.000# 0.063# 0.091f + + + Total SPIN, PARITY = 12.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.4 12.3 + S-matrix 1 = 0.99997 0.00017 for L= 12, J= 12.5 channel on core I = 0.0 from L= 12, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.005 0.001 deg. for the L = 12, J = 12.5 channel. + 12.5 0.99996803 0.00016947i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 12.5+/12 @ 1 = 0.023 , Out: 0.000# 0.004# 0.023f + + + Total SPIN, PARITY = 12.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.4 12.3 + S-matrix 1 = 0.99999 0.00004 for L= 13, J= 12.5 channel on core I = 0.0 from L= 13, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.001 0.000 deg. for the L = 13, J = 12.5 channel. + 12.5 0.99999248 0.00004434i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 12.5-/13 @ 1 = 5.350/, Out: 0.000# 0.000# 0.005f + + + Total SPIN, PARITY = 13.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.4 13.2 + S-matrix 1 = 1.00000 0.00001 for L= 14, J= 13.5 channel on core I = 0.0 from L= 14, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 14, J = 13.5 channel. + 13.5 0.99999823 0.00001256i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 13.5+/14 @ 1 = 1.359/, Out: 0.000# 0.000# 0.001f + + + Total SPIN, PARITY = 13.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.4 13.2 + S-matrix 1 = 0.99999 0.00005 for L= 13, J= 13.5 channel on core I = 0.0 from L= 13, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.001 0.000 deg. for the L = 13, J = 13.5 channel. + 13.5 0.99999255 0.00004763i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 13.5-/13 @ 1 = 5.705/, Out: 0.000# 0.000# 0.006f + + + Total SPIN, PARITY = 14.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.4 14.1 + S-matrix 1 = 1.00000 0.00001 for L= 14, J= 14.5 channel on core I = 0.0 from L= 14, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 14, J = 14.5 channel. + 14.5 0.99999825 0.00001336i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 14.5+/14 @ 1 = 1.441/, Out: 0.000# 0.000# 0.001f + + + Total SPIN, PARITY = 14.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.4 14.1 + S-matrix 1 = 1.00000 0.00000 for L= 15, J= 14.5 channel on core I = 0.0 from L= 15, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 15, J = 14.5 channel. + 14.5 0.99999958 0.00000355i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 14.5-/15 @ 1 = 0.348/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 15.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.5 15.1 + S-matrix 1 = 1.00000 0.00000 for L= 16, J= 15.5 channel on core I = 0.0 from L= 16, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 16, J = 15.5 channel. + 15.5 0.99999990 0.00000100i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 15.5+/16 @ 1 = 0.090/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 15.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.5 15.1 + S-matrix 1 = 1.00000 0.00000 for L= 15, J= 15.5 channel on core I = 0.0 from L= 15, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 15, J = 15.5 channel. + 15.5 0.99999958 0.00000375i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 15.5-/15 @ 1 = 0.367/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 16.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.5 16.0 + S-matrix 1 = 1.00000 0.00000 for L= 16, J= 16.5 channel on core I = 0.0 from L= 16, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 16, J = 16.5 channel. + 16.5 0.99999990 0.00000105i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 16.5+/16 @ 1 = 0.094/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 16.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.5 16.0 + S-matrix 1 = 1.00000 0.00000 for L= 17, J= 16.5 channel on core I = 0.0 from L= 17, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 17, J = 16.5 channel. + 16.5 0.99999997 0.00000028i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 16.5-/17 @ 1 = 0.023/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 17.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.5 16.9 + S-matrix 1 = 1.00000 0.00000 for L= 18, J= 17.5 channel on core I = 0.0 from L= 18, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 18, J = 17.5 channel. + 17.5 0.99999999 0.00000008i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 17.5+/18 @ 1 = 6.171#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 17.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.5 16.9 + S-matrix 1 = 1.00000 0.00000 for L= 17, J= 17.5 channel on core I = 0.0 from L= 17, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 17, J = 17.5 channel. + 17.5 0.99999998 0.00000029i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 17.5-/17 @ 1 = 0.025/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 18.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.6 17.9 + S-matrix 1 = 1.00000 0.00000 for L= 18, J= 18.5 channel on core I = 0.0 from L= 18, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 18, J = 18.5 channel. + 18.5 0.99999999 0.00000008i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 18.5+/18 @ 1 = 6.447#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 18.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.6 17.9 + S-matrix 1 = 1.00000 0.00000 for L= 19, J= 18.5 channel on core I = 0.0 from L= 19, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 19, J = 18.5 channel. + 18.5 1.00000000 0.00000002i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 18.5-/19 @ 1 = 1.630#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 19.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.6 18.8 + S-matrix 1 = 1.00000 0.00000 for L= 20, J= 19.5 channel on core I = 0.0 from L= 20, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 20, J = 19.5 channel. + 19.5 1.00000000 0.00000001i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 19.5+/20 @ 1 = 0.428#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 19.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.6 18.8 + S-matrix 1 = 1.00000 0.00000 for L= 19, J= 19.5 channel on core I = 0.0 from L= 19, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 19, J = 19.5 channel. + 19.5 1.00000000 0.00000002i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 19.5-/19 @ 1 = 1.699#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 20.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.6 19.7 + S-matrix 1 = 1.00000 0.00000 for L= 20, J= 20.5 channel on core I = 0.0 from L= 20, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 20, J = 20.5 channel. + 20.5 1.00000000 0.00000001i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 20.5+/20 @ 1 = 0.445#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 20.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.6 19.7 + S-matrix 1 = 1.00000 0.00000 for L= 21, J= 20.5 channel on core I = 0.0 from L= 21, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 21, J = 20.5 channel. + 20.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 20.5-/21 @ 1 = 0.110#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 21.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.7 20.7 + S-matrix 1 = 1.00000 0.00000 for L= 22, J= 21.5 channel on core I = 0.0 from L= 22, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 22, J = 21.5 channel. + 21.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 21.5+/22 @ 1 = 0.027#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 21.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.7 20.7 + S-matrix 1 = 1.00000 0.00000 for L= 21, J= 21.5 channel on core I = 0.0 from L= 21, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 21, J = 21.5 channel. + 21.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 21.5-/21 @ 1 = 0.114#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 22.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.7 21.6 + S-matrix 1 = 1.00000 0.00000 for L= 22, J= 22.5 channel on core I = 0.0 from L= 22, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 22, J = 22.5 channel. + 22.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 22.5+/22 @ 1 = 0.028#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 22.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.7 21.6 + S-matrix 1 = 1.00000 0.00000 for L= 23, J= 22.5 channel on core I = 0.0 from L= 23, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 23, J = 22.5 channel. + 22.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 22.5-/23 @ 1 = 0.006#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 23.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.7 22.5 + S-matrix 1 = 1.00000 0.00000 for L= 24, J= 23.5 channel on core I = 0.0 from L= 24, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 24, J = 23.5 channel. + 23.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 23.5+/24 @ 1 = 0.001#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 23.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.7 22.5 + S-matrix 1 = 1.00000 0.00000 for L= 23, J= 23.5 channel on core I = 0.0 from L= 23, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 23, J = 23.5 channel. + 23.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 23.5-/23 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 24.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.8 23.5 + S-matrix 1 = 1.00000 0.00000 for L= 24, J= 24.5 channel on core I = 0.0 from L= 24, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 24, J = 24.5 channel. + 24.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 24.5+/24 @ 1 = 0.001#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 24.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.8 23.5 + S-matrix 1 = 1.00000 0.00000 for L= 25, J= 24.5 channel on core I = 0.0 from L= 25, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 25, J = 24.5 channel. + 24.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 24.5-/25 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 25.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.8 24.4 + S-matrix 1 = 1.00000 0.00000 for L= 26, J= 25.5 channel on core I = 0.0 from L= 26, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 26, J = 25.5 channel. + 25.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 25.5+/26 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 25.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.8 24.4 + S-matrix 1 = 1.00000 0.00000 for L= 25, J= 25.5 channel on core I = 0.0 from L= 25, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 25, J = 25.5 channel. + 25.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 25.5-/25 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 26.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.8 25.3 + S-matrix 1 = 1.00000 0.00000 for L= 26, J= 26.5 channel on core I = 0.0 from L= 26, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 26, J = 26.5 channel. + 26.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 26.5+/26 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 26.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.8 25.3 + S-matrix 1 = 1.00000 0.00000 for L= 27, J= 26.5 channel on core I = 0.0 from L= 27, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 27, J = 26.5 channel. + 26.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 26.5-/27 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 27.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.9 26.3 + S-matrix 1 = 1.00000 0.00000 for L= 28, J= 27.5 channel on core I = 0.0 from L= 28, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 28, J = 27.5 channel. + 27.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 27.5+/28 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 27.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.9 26.3 + S-matrix 1 = 1.00000 0.00000 for L= 27, J= 27.5 channel on core I = 0.0 from L= 27, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 27, J = 27.5 channel. + 27.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 27.5-/27 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 28.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.9 27.2 + S-matrix 1 = 1.00000 0.00000 for L= 28, J= 28.5 channel on core I = 0.0 from L= 28, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 28, J = 28.5 channel. + 28.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 28.5+/28 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 28.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.9 27.2 + S-matrix 1 = 1.00000 0.00000 for L= 29, J= 28.5 channel on core I = 0.0 from L= 29, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 29, J = 28.5 channel. + 28.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 28.5-/29 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 29.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.9 28.1 + S-matrix 1 = 1.00000 0.00000 for L= 30, J= 29.5 channel on core I = 0.0 from L= 30, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 30, J = 29.5 channel. + 29.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 29.5+/30 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 29.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.9 28.1 + S-matrix 1 = 1.00000 0.00000 for L= 29, J= 29.5 channel on core I = 0.0 from L= 29, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 29, J = 29.5 channel. + 29.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 29.5-/29 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 30.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 29.1 + S-matrix 1 = 1.00000 0.00000 for L= 30, J= 30.5 channel on core I = 0.0 from L= 30, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 30, J = 30.5 channel. + 30.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 30.5+/30 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 30.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 29.1 + S-matrix 1 = 1.00000 0.00000 for L= 31, J= 30.5 channel on core I = 0.0 from L= 31, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 31, J = 30.5 channel. + 30.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 30.5-/31 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 31.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 30.0 + S-matrix 1 = 1.00000 -0.00000 for L= 32, J= 31.5 channel on core I = 0.0 from L= 32, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 32, J = 31.5 channel. + 31.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 31.5+/32 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 31.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 30.0 + S-matrix 1 = 1.00000 0.00000 for L= 31, J= 31.5 channel on core I = 0.0 from L= 31, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 31, J = 31.5 channel. + 31.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 31.5-/31 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 32.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 30.9 + S-matrix 1 = 1.00000 0.00000 for L= 32, J= 32.5 channel on core I = 0.0 from L= 32, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 32, J = 32.5 channel. + 32.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 32.5+/32 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 32.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 30.9 + S-matrix 1 = 1.00000 0.00000 for L= 33, J= 32.5 channel on core I = 0.0 from L= 33, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 33, J = 32.5 channel. + 32.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 32.5-/33 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 33.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 31.9 + S-matrix 1 = 1.00000 0.00000 for L= 34, J= 33.5 channel on core I = 0.0 from L= 34, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 34, J = 33.5 channel. + 33.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 33.5+/34 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 33.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 31.9 + S-matrix 1 = 1.00000 0.00000 for L= 33, J= 33.5 channel on core I = 0.0 from L= 33, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 33, J = 33.5 channel. + 33.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 33.5-/33 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 34.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.1 32.8 + S-matrix 1 = 1.00000 -0.00000 for L= 34, J= 34.5 channel on core I = 0.0 from L= 34, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 34, J = 34.5 channel. + 34.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 34.5+/34 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 34.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.1 32.8 + S-matrix 1 = 1.00000 0.00000 for L= 35, J= 34.5 channel on core I = 0.0 from L= 35, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 35, J = 34.5 channel. + 34.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 34.5-/35 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 35.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.1 33.7 + S-matrix 1 = 1.00000 -0.00000 for L= 36, J= 35.5 channel on core I = 0.0 from L= 36, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 36, J = 35.5 channel. + 35.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 35.5+/36 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 35.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.1 33.7 + S-matrix 1 = 1.00000 -0.00000 for L= 35, J= 35.5 channel on core I = 0.0 from L= 35, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 35, J = 35.5 channel. + 35.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 35.5-/35 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 36.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.1 34.7 + S-matrix 1 = 1.00000 -0.00000 for L= 36, J= 36.5 channel on core I = 0.0 from L= 36, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 36, J = 36.5 channel. + 36.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 36.5+/36 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 36.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.1 34.7 + S-matrix 1 = 1.00000 0.00000 for L= 37, J= 36.5 channel on core I = 0.0 from L= 37, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 37, J = 36.5 channel. + 36.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 36.5-/37 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 37.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.2 35.6 + S-matrix 1 = 1.00000 -0.00000 for L= 38, J= 37.5 channel on core I = 0.0 from L= 38, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 38, J = 37.5 channel. + 37.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 37.5+/38 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 37.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.2 35.6 + S-matrix 1 = 1.00000 0.00000 for L= 37, J= 37.5 channel on core I = 0.0 from L= 37, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 37, J = 37.5 channel. + 37.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 37.5-/37 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 38.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.2 36.5 + S-matrix 1 = 1.00000 0.00000 for L= 38, J= 38.5 channel on core I = 0.0 from L= 38, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 38, J = 38.5 channel. + 38.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 38.5+/38 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 38.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.2 36.5 + S-matrix 1 = 1.00000 -0.00000 for L= 39, J= 38.5 channel on core I = 0.0 from L= 39, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 39, J = 38.5 channel. + 38.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 38.5-/39 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 39.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.2 37.5 + S-matrix 1 = 1.00000 0.00000 for L= 40, J= 39.5 channel on core I = 0.0 from L= 40, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 40, J = 39.5 channel. + 39.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 39.5+/40 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 39.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.2 37.5 + S-matrix 1 = 1.00000 0.00000 for L= 39, J= 39.5 channel on core I = 0.0 from L= 39, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 39, J = 39.5 channel. + 39.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 39.5-/39 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 40.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.3 38.4 + S-matrix 1 = 1.00000 -0.00000 for L= 40, J= 40.5 channel on core I = 0.0 from L= 40, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 40, J = 40.5 channel. + 40.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 40.5+/40 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 40.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.3 38.4 + S-matrix 1 = 1.00000 -0.00000 for L= 41, J= 40.5 channel on core I = 0.0 from L= 41, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 41, J = 40.5 channel. + 40.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 40.5-/41 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + Finished all CC sets @ 1.48799988E-02 +0CUMULATIVE REACTION cross section = 1082.71564 = 4.19 = 20.7 +0CUMULATIVE ELASTIC cross section = 0.00000 +0CUMULATIVE outgoing cross sections in partition 1 : 0.00000 +0CUMULATIVE outgoing cross sections in partition 2 : 10.18768 +0Cumulative ABSORBTION by Imaginary Potentials = 1072.52795 = 4.20 = 20.8 + Fusion for specific p M-states : 1072.527953 1072.527953 +0CUMULATIVE OUTGOING cross section = 10.18768 + Strength functions * 10^4 for L=0-2 = 0.27505 0.27649 2.73902 (with r0= 1.35 fm). R' = 0.653 fm + To convert to S-factors (MeV.mb = keV.b), multiply by 1.2822E+03 + + + CROSS SECTIONS FOR OUTGOING p & 48Ca in state # 1 with spins & parities 0.5 + & 0.0 +; 0 + + 0.01 deg.: X-S = 1.490336E+16 mb/sr, & pols = 0. 0.00000 ++ /R = 1.000000E+00 + 1.00 deg.: X-S = 1.495732E+08 mb/sr, & pols = 0. 0.00000 ++ /R = 1.003569E+00 + 2.00 deg.: X-S = 9.237209E+06 mb/sr, & pols = 0. 0.00007 ++ /R = 9.914901E-01 + 3.00 deg.: X-S = 1.759892E+06 mb/sr, & pols = 0. 0.00022 ++ /R = 9.560674E-01 + 4.00 deg.: X-S = 5.268166E+05 mb/sr, & pols = 0. 0.00043 ++ /R = 9.041958E-01 + 5.00 deg.: X-S = 2.016894E+05 mb/sr, & pols =-0. 0.00058 ++ /R = 8.447491E-01 + 6.00 deg.: X-S = 9.053448E+04 mb/sr, & pols =-0. 0.00053 ++ /R = 7.858524E-01 + 7.00 deg.: X-S = 4.568107E+04 mb/sr, & pols =-0. 0.00004 ++ /R = 7.341145E-01 + 8.00 deg.: X-S = 2.534806E+04 mb/sr, & pols =-0. -0.00115 ++ /R = 6.943992E-01 + 9.00 deg.: X-S = 1.527710E+04 mb/sr, & pols =-0. -0.00323 ++ /R = 6.697929E-01 + 10.00 deg.: X-S = 9911.202234 mb/sr, & pols = 0. -0.00628 ++ /R = 6.616628E-01 + 11.00 deg.: X-S = 6859.832582 mb/sr, & pols = 0. -0.01019 ++ /R = 6.697787E-01 + 12.00 deg.: X-S = 5013.559815 mb/sr, & pols =-0. -0.01469 ++ /R = 6.924857E-01 + 13.00 deg.: X-S = 3825.848048 mb/sr, & pols =-0. -0.01949 ++ /R = 7.269242E-01 + 14.00 deg.: X-S = 3014.303502 mb/sr, & pols = 0. -0.02431 ++ /R = 7.692920E-01 + 15.00 deg.: X-S = 2427.258182 mb/sr, & pols = 0. -0.02898 ++ /R = 8.151415E-01 + 16.00 deg.: X-S = 1980.611722 mb/sr, & pols = 0. -0.03340 ++ /R = 8.597000E-01 + 17.00 deg.: X-S = 1626.444277 mb/sr, & pols = 0. -0.03755 ++ /R = 8.982017E-01 + 18.00 deg.: X-S = 1336.767962 mb/sr, & pols =-0. -0.04140 ++ /R = 9.262157E-01 + 19.00 deg.: X-S = 1094.820317 mb/sr, & pols = 0. -0.04493 ++ /R = 9.399530E-01 + 20.00 deg.: X-S = 890.262226 mb/sr, & pols = 0. -0.04810 ++ /R = 9.365376E-01 + 21.00 deg.: X-S = 716.465688 mb/sr, & pols =-0. -0.05079 ++ /R = 9.142268E-01 + 22.00 deg.: X-S = 568.953343 mb/sr, & pols = 0. -0.05280 ++ /R = 8.725662E-01 + 23.00 deg.: X-S = 444.489260 mb/sr, & pols =-0. -0.05379 ++ /R = 8.124729E-01 + 24.00 deg.: X-S = 340.546317 mb/sr, & pols =-0. -0.05312 ++ /R = 7.362379E-01 + 25.00 deg.: X-S = 254.995587 mb/sr, & pols =-0. -0.04972 ++ /R = 6.474497E-01 + 26.00 deg.: X-S = 185.928629 mb/sr, & pols =-0. -0.04164 ++ /R = 5.508391E-01 + 27.00 deg.: X-S = 131.560221 mb/sr, & pols =-0. -0.02538 ++ /R = 4.520557E-01 + 28.00 deg.: X-S = 90.179959 mb/sr, & pols =-0. 0.00542 ++ /R = 3.573856E-01 + 29.00 deg.: X-S = 60.133348 mb/sr, & pols =-0. 0.06180 ++ /R = 2.734263E-01 + 30.00 deg.: X-S = 39.820281 mb/sr, & pols =-0. 0.15903 ++ /R = 2.067358E-01 + 31.00 deg.: X-S = 27.703183 mb/sr, & pols =-0. 0.30100 ++ /R = 1.634750E-01 + 32.00 deg.: X-S = 22.319867 mb/sr, & pols =-0. 0.43681 ++ /R = 1.490631E-01 + 33.00 deg.: X-S = 22.297861 mb/sr, & pols =-0. 0.47500 ++ /R = 1.678637E-01 + 34.00 deg.: X-S = 26.368132 mb/sr, & pols =-0. 0.41339 ++ /R = 2.229190E-01 + 35.00 deg.: X-S = 33.376885 mb/sr, & pols =-0. 0.32122 ++ /R = 3.157471E-01 + 36.00 deg.: X-S = 42.294648 mb/sr, & pols = 0. 0.23895 ++ /R = 4.462120E-01 + 37.00 deg.: X-S = 52.222242 mb/sr, & pols = 0. 0.17451 ++ /R = 6.124740E-01 + 38.00 deg.: X-S = 62.393494 mb/sr, & pols = 0. 0.12508 ++ /R = 8.110233E-01 + 39.00 deg.: X-S = 72.174777 mb/sr, & pols = 0. 0.08649 ++ /R = 1.036795E+00 + 40.00 deg.: X-S = 81.061577 mb/sr, & pols = 0. 0.05542 ++ /R = 1.283359E+00 + 41.00 deg.: X-S = 88.672411 mb/sr, & pols = 0. 0.02954 ++ /R = 1.543178E+00 + 42.00 deg.: X-S = 94.740499 mb/sr, & pols = 0. 0.00722 ++ /R = 1.807918E+00 + 43.00 deg.: X-S = 99.103600 mb/sr, & pols = 0. -0.01266 ++ /R = 2.068795E+00 + 44.00 deg.: X-S = 101.692487 mb/sr, & pols = 0. -0.03092 ++ /R = 2.316951E+00 + 45.00 deg.: X-S = 102.518496 mb/sr, & pols = 0. -0.04814 ++ /R = 2.543825E+00 + 46.00 deg.: X-S = 101.660589 mb/sr, & pols = 0. -0.06478 ++ /R = 2.741518E+00 + 47.00 deg.: X-S = 99.252330 mb/sr, & pols = 0. -0.08122 ++ /R = 2.903125E+00 + 48.00 deg.: X-S = 95.469139 mb/sr, & pols =-0. -0.09776 ++ /R = 3.023031E+00 + 49.00 deg.: X-S = 90.516135 mb/sr, & pols = 0. -0.11469 ++ /R = 3.097135E+00 + 50.00 deg.: X-S = 84.616830 mb/sr, & pols = 0. -0.13226 ++ /R = 3.123030E+00 + 51.00 deg.: X-S = 78.002886 mb/sr, & pols =-0. -0.15073 ++ /R = 3.100091E+00 + 52.00 deg.: X-S = 70.905079 mb/sr, & pols =-0. -0.17036 ++ /R = 3.029504E+00 + 53.00 deg.: X-S = 63.545568 mb/sr, & pols =-0. -0.19141 ++ /R = 2.914214E+00 + 54.00 deg.: X-S = 56.131535 mb/sr, & pols =-0. -0.21415 ++ /R = 2.758801E+00 + 55.00 deg.: X-S = 48.850172 mb/sr, & pols =-0. -0.23887 ++ /R = 2.569300E+00 + 56.00 deg.: X-S = 41.865002 mb/sr, & pols =-0. -0.26580 ++ /R = 2.352960E+00 + 57.00 deg.: X-S = 35.313454 mb/sr, & pols =-0. -0.29511 ++ /R = 2.117962E+00 + 58.00 deg.: X-S = 29.305595 mb/sr, & pols =-0. -0.32676 ++ /R = 1.873104E+00 + 59.00 deg.: X-S = 23.923900 mb/sr, & pols =-0. -0.36027 ++ /R = 1.627472E+00 + 60.00 deg.: X-S = 19.223924 mb/sr, & pols =-0. -0.39427 ++ /R = 1.390108E+00 + 61.00 deg.: X-S = 15.235743 mb/sr, & pols =-0. -0.42565 ++ /R = 1.169676E+00 + 62.00 deg.: X-S = 11.966002 mb/sr, & pols =-0. -0.44828 ++ /R = 9.741656E-01 + 63.00 deg.: X-S = 9.400437 mb/sr, & pols = 0. -0.45131 ++ /R = 8.106153E-01 + 64.00 deg.: X-S = 7.506729 mb/sr, & pols = 0. -0.41892 ++ /R = 6.848823E-01 + 65.00 deg.: X-S = 6.237552 mb/sr, & pols = 0. -0.33551 ++ /R = 6.014598E-01 + 66.00 deg.: X-S = 5.533707 mb/sr, & pols = 0. -0.19984 ++ /R = 5.633490E-01 + 67.00 deg.: X-S = 5.327225 mb/sr, & pols = 0. -0.03758 ++ /R = 5.719860E-01 + 68.00 deg.: X-S = 5.544362 mb/sr, & pols = 0. 0.11021 ++ /R = 6.272267E-01 + 69.00 deg.: X-S = 6.108391 mb/sr, & pols = 0. 0.21580 ++ /R = 7.273866E-01 + 70.00 deg.: X-S = 6.942156 mb/sr, & pols = 0. 0.27581 ++ /R = 8.693325E-01 + 71.00 deg.: X-S = 7.970320 mb/sr, & pols = 0. 0.30075 ++ /R = 1.048620E+00 + 72.00 deg.: X-S = 9.121301 mb/sr, & pols = 0. 0.30314 ++ /R = 1.259673E+00 + 73.00 deg.: X-S = 10.328859 mb/sr, & pols = 0. 0.29247 ++ /R = 1.495991E+00 + 74.00 deg.: X-S = 11.533343 mb/sr, & pols = 0. 0.27481 ++ /R = 1.750385E+00 + 75.00 deg.: X-S = 12.682599 mb/sr, & pols = 0. 0.25371 ++ /R = 2.015223E+00 + 76.00 deg.: X-S = 13.732565 mb/sr, & pols = 0. 0.23117 ++ /R = 2.282685E+00 + 77.00 deg.: X-S = 14.647554 mb/sr, & pols = 0. 0.20824 ++ /R = 2.545011E+00 + 78.00 deg.: X-S = 15.400284 mb/sr, & pols = 0. 0.18544 ++ /R = 2.794746E+00 + 79.00 deg.: X-S = 15.971653 mb/sr, & pols = 0. 0.16302 ++ /R = 3.024951E+00 + 80.00 deg.: X-S = 16.350331 mb/sr, & pols = 0. 0.14105 ++ /R = 3.229408E+00 + 81.00 deg.: X-S = 16.532172 mb/sr, & pols =-0. 0.11950 ++ /R = 3.402780E+00 + 82.00 deg.: X-S = 16.519501 mb/sr, & pols =-0. 0.09831 ++ /R = 3.540747E+00 + 83.00 deg.: X-S = 16.320312 mb/sr, & pols =-0. 0.07740 ++ /R = 3.640098E+00 + 84.00 deg.: X-S = 15.947400 mb/sr, & pols =-0. 0.05665 ++ /R = 3.698791E+00 + 85.00 deg.: X-S = 15.417461 mb/sr, & pols =-0. 0.03599 ++ /R = 3.715974E+00 + 86.00 deg.: X-S = 14.750200 mb/sr, & pols =-0. 0.01531 ++ /R = 3.691965E+00 + 87.00 deg.: X-S = 13.967446 mb/sr, & pols =-0. -0.00546 ++ /R = 3.628208E+00 + 88.00 deg.: X-S = 13.092328 mb/sr, & pols =-0. -0.02641 ++ /R = 3.527186E+00 + 89.00 deg.: X-S = 12.148493 mb/sr, & pols =-0. -0.04757 ++ /R = 3.392313E+00 + 90.00 deg.: X-S = 11.159410 mb/sr, & pols =-0. -0.06896 ++ /R = 3.227807E+00 + 91.00 deg.: X-S = 10.147751 mb/sr, & pols =-0. -0.09056 ++ /R = 3.038536E+00 + 92.00 deg.: X-S = 9.134862 mb/sr, & pols =-0. -0.11226 ++ /R = 2.829858E+00 + 93.00 deg.: X-S = 8.140329 mb/sr, & pols =-0. -0.13387 ++ /R = 2.607457E+00 + 94.00 deg.: X-S = 7.181633 mb/sr, & pols = 0. -0.15501 ++ /R = 2.377166E+00 + 95.00 deg.: X-S = 6.273902 mb/sr, & pols = 0. -0.17511 ++ /R = 2.144804E+00 + 96.00 deg.: X-S = 5.429744 mb/sr, & pols = 0. -0.19322 ++ /R = 1.916018E+00 + 97.00 deg.: X-S = 4.659170 mb/sr, & pols = 0. -0.20790 ++ /R = 1.696131E+00 + 98.00 deg.: X-S = 3.969581 mb/sr, & pols = 0. -0.21700 ++ /R = 1.490015E+00 + 99.00 deg.: X-S = 3.365838 mb/sr, & pols = 0. -0.21748 ++ /R = 1.301972E+00 + 100.00 deg.: X-S = 2.850370 mb/sr, & pols = 0. -0.20532 ++ /R = 1.135647E+00 + 101.00 deg.: X-S = 2.423351 mb/sr, & pols = 0. -0.17570 ++ /R = 9.939553E-01 + 102.00 deg.: X-S = 2.082903 mb/sr, & pols = 0. -0.12400 ++ /R = 8.790340E-01 + 103.00 deg.: X-S = 1.825335 mb/sr, & pols = 0. -0.04771 ++ /R = 7.922209E-01 + 104.00 deg.: X-S = 1.645404 mb/sr, & pols = 0. 0.05089 ++ /R = 7.340531E-01 + 105.00 deg.: X-S = 1.536589 mb/sr, & pols = 0. 0.16304 ++ /R = 7.042888E-01 + 106.00 deg.: X-S = 1.491368 mb/sr, & pols = 0. 0.27500 ++ /R = 7.019491E-01 + 107.00 deg.: X-S = 1.501493 mb/sr, & pols = 0. 0.37283 ++ /R = 7.253785E-01 + 108.00 deg.: X-S = 1.558263 mb/sr, & pols = 0. 0.44753 ++ /R = 7.723206E-01 + 109.00 deg.: X-S = 1.652771 mb/sr, & pols = 0. 0.49683 ++ /R = 8.400073E-01 + 110.00 deg.: X-S = 1.776144 mb/sr, & pols = 0. 0.52360 ++ /R = 9.252575E-01 + 111.00 deg.: X-S = 1.919755 mb/sr, & pols = 0. 0.53297 ++ /R = 1.024583E+00 + 112.00 deg.: X-S = 2.075405 mb/sr, & pols = 0. 0.53025 ++ /R = 1.134295E+00 + 113.00 deg.: X-S = 2.235487 mb/sr, & pols = 0. 0.51980 ++ /R = 1.250618E+00 + 114.00 deg.: X-S = 2.393111 mb/sr, & pols = 0. 0.50490 ++ /R = 1.369792E+00 + 115.00 deg.: X-S = 2.542206 mb/sr, & pols = 0. 0.48778 ++ /R = 1.488174E+00 + 116.00 deg.: X-S = 2.677591 mb/sr, & pols = 0. 0.46992 ++ /R = 1.602332E+00 + 117.00 deg.: X-S = 2.795014 mb/sr, & pols = 0. 0.45228 ++ /R = 1.709124E+00 + 118.00 deg.: X-S = 2.891171 mb/sr, & pols = 0. 0.43543 ++ /R = 1.805770E+00 + 119.00 deg.: X-S = 2.963693 mb/sr, & pols = 0. 0.41970 ++ /R = 1.889910E+00 + 120.00 deg.: X-S = 3.011115 mb/sr, & pols = 0. 0.40527 ++ /R = 1.959640E+00 + 121.00 deg.: X-S = 3.032822 mb/sr, & pols = 0. 0.39224 ++ /R = 2.013541E+00 + 122.00 deg.: X-S = 3.028983 mb/sr, & pols = 0. 0.38060 ++ /R = 2.050691E+00 + 123.00 deg.: X-S = 3.000466 mb/sr, & pols = 0. 0.37033 ++ /R = 2.070661E+00 + 124.00 deg.: X-S = 2.948747 mb/sr, & pols = 0. 0.36136 ++ /R = 2.073498E+00 + 125.00 deg.: X-S = 2.875810 mb/sr, & pols = 0. 0.35360 ++ /R = 2.059692E+00 + 126.00 deg.: X-S = 2.784045 mb/sr, & pols = 0. 0.34692 ++ /R = 2.030141E+00 + 127.00 deg.: X-S = 2.676142 mb/sr, & pols = 0. 0.34117 ++ /R = 1.986096E+00 + 128.00 deg.: X-S = 2.554990 mb/sr, & pols = 0. 0.33617 ++ /R = 1.929107E+00 + 129.00 deg.: X-S = 2.423584 mb/sr, & pols = 0. 0.33169 ++ /R = 1.860962E+00 + 130.00 deg.: X-S = 2.284928 mb/sr, & pols = 0. 0.32746 ++ /R = 1.783618E+00 + 131.00 deg.: X-S = 2.141961 mb/sr, & pols = 0. 0.32315 ++ /R = 1.699141E+00 + 132.00 deg.: X-S = 1.997481 mb/sr, & pols = 0. 0.31837 ++ /R = 1.609643E+00 + 133.00 deg.: X-S = 1.854089 mb/sr, & pols = 0. 0.31267 ++ /R = 1.517218E+00 + 134.00 deg.: X-S = 1.714141 mb/sr, & pols = 0. 0.30553 ++ /R = 1.423892E+00 + 135.00 deg.: X-S = 1.579711 mb/sr, & pols = 0. 0.29636 ++ /R = 1.331574E+00 + 136.00 deg.: X-S = 1.452573 mb/sr, & pols = 0. 0.28456 ++ /R = 1.242018E+00 + 137.00 deg.: X-S = 1.334183 mb/sr, & pols = 0. 0.26955 ++ /R = 1.156787E+00 + 138.00 deg.: X-S = 1.225687 mb/sr, & pols = 0. 0.25081 ++ /R = 1.077241E+00 + 139.00 deg.: X-S = 1.127923 mb/sr, & pols =-0. 0.22803 ++ /R = 1.004515E+00 + 140.00 deg.: X-S = 1.041446 mb/sr, & pols =-0. 0.20121 ++ /R = 9.395207E-01 + 141.00 deg.: X-S = 0.966547 mb/sr, & pols =-0. 0.17080 ++ /R = 8.829492E-01 + 142.00 deg.: X-S = 0.903291 mb/sr, & pols =-0. 0.13779 ++ /R = 8.352840E-01 + 143.00 deg.: X-S = 0.851543 mb/sr, & pols =-0. 0.10380 ++ /R = 7.968182E-01 + 144.00 deg.: X-S = 0.811012 mb/sr, & pols =-0. 0.07099 ++ /R = 7.676781E-01 + 145.00 deg.: X-S = 0.781286 mb/sr, & pols =-0. 0.04183 ++ /R = 7.478496E-01 + 146.00 deg.: X-S = 0.761863 mb/sr, & pols =-0. 0.01886 ++ /R = 7.372058E-01 + 147.00 deg.: X-S = 0.752194 mb/sr, & pols =-0. 0.00423 ++ /R = 7.355362E-01 + 148.00 deg.: X-S = 0.751704 mb/sr, & pols =-0. -0.00054 ++ /R = 7.425740E-01 + 149.00 deg.: X-S = 0.759823 mb/sr, & pols =-0. 0.00518 ++ /R = 7.580207E-01 + 150.00 deg.: X-S = 0.776007 mb/sr, & pols =-0. 0.02113 ++ /R = 7.815682E-01 + 151.00 deg.: X-S = 0.799747 mb/sr, & pols =-0. 0.04623 ++ /R = 8.129149E-01 + 152.00 deg.: X-S = 0.830584 mb/sr, & pols =-0. 0.07885 ++ /R = 8.517784E-01 + 153.00 deg.: X-S = 0.868113 mb/sr, & pols =-0. 0.11700 ++ /R = 8.979010E-01 + 154.00 deg.: X-S = 0.911972 mb/sr, & pols =-0. 0.15859 ++ /R = 9.510509E-01 + 155.00 deg.: X-S = 0.961848 mb/sr, & pols =-0. 0.20157 ++ /R = 1.011018E+00 + 156.00 deg.: X-S = 1.017453 mb/sr, & pols =-0. 0.24408 ++ /R = 1.077602E+00 + 157.00 deg.: X-S = 1.078521 mb/sr, & pols =-0. 0.28451 ++ /R = 1.150604E+00 + 158.00 deg.: X-S = 1.144783 mb/sr, & pols =-0. 0.32154 ++ /R = 1.229804E+00 + 159.00 deg.: X-S = 1.215953 mb/sr, & pols =-0. 0.35414 ++ /R = 1.314945E+00 + 160.00 deg.: X-S = 1.291710 mb/sr, & pols =-0. 0.38157 ++ /R = 1.405715E+00 + 161.00 deg.: X-S = 1.371679 mb/sr, & pols =-0. 0.40334 ++ /R = 1.501723E+00 + 162.00 deg.: X-S = 1.455419 mb/sr, & pols =-0. 0.41920 ++ /R = 1.602486E+00 + 163.00 deg.: X-S = 1.542408 mb/sr, & pols =-0. 0.42909 ++ /R = 1.707413E+00 + 164.00 deg.: X-S = 1.632034 mb/sr, & pols =-0. 0.43312 ++ /R = 1.815794E+00 + 165.00 deg.: X-S = 1.723591 mb/sr, & pols =-0. 0.43152 ++ /R = 1.926792E+00 + 166.00 deg.: X-S = 1.816281 mb/sr, & pols =-0. 0.42461 ++ /R = 2.039446E+00 + 167.00 deg.: X-S = 1.909214 mb/sr, & pols =-0. 0.41278 ++ /R = 2.152673E+00 + 168.00 deg.: X-S = 2.001422 mb/sr, & pols =-0. 0.39645 ++ /R = 2.265283E+00 + 169.00 deg.: X-S = 2.091868 mb/sr, & pols =-0. 0.37609 ++ /R = 2.375989E+00 + 170.00 deg.: X-S = 2.179466 mb/sr, & pols =-0. 0.35212 ++ /R = 2.483438E+00 + 171.00 deg.: X-S = 2.263103 mb/sr, & pols =-0. 0.32501 ++ /R = 2.586231E+00 + 172.00 deg.: X-S = 2.341662 mb/sr, & pols =-0. 0.29516 ++ /R = 2.682957E+00 + 173.00 deg.: X-S = 2.414046 mb/sr, & pols =-0. 0.26297 ++ /R = 2.772227E+00 + 174.00 deg.: X-S = 2.479207 mb/sr, & pols =-0. 0.22881 ++ /R = 2.852704E+00 + 175.00 deg.: X-S = 2.536170 mb/sr, & pols =-0. 0.19303 ++ /R = 2.923146E+00 + 176.00 deg.: X-S = 2.584061 mb/sr, & pols =-0. 0.15593 ++ /R = 2.982432E+00 + 177.00 deg.: X-S = 2.622131 mb/sr, & pols =-0. 0.11781 ++ /R = 3.029600E+00 + 178.00 deg.: X-S = 2.649772 mb/sr, & pols =-0. 0.07895 ++ /R = 3.063870E+00 + 179.00 deg.: X-S = 2.666539 mb/sr, & pols =-0. 0.03959 ++ /R = 3.084666E+00 + 179.99 deg.: X-S = 2.672158 mb/sr, & pols =-0. 0.00040 ++ /R = 3.091637E+00 + Integrated 2.8525E+11 mb, over [ 0.000, 180.000] at 25.0000 MeV + + CROSS SECTIONS FOR OUTGOING n & 48Sc in state # 1 with spins & parities 0.5 + & 0.0 +; 0 + + 0.00 deg.: X-S = 2.442289 mb/sr, & pols =-0. 0.00000 ++ REAC 1.0188E+01 mb + 1.00 deg.: X-S = 2.444024 mb/sr, & pols = 0. 0.00922 + 2.00 deg.: X-S = 2.449206 mb/sr, & pols =-0. 0.01826 + 3.00 deg.: X-S = 2.457768 mb/sr, & pols =-0. 0.02693 + 4.00 deg.: X-S = 2.469594 mb/sr, & pols =-0. 0.03508 + 5.00 deg.: X-S = 2.484523 mb/sr, & pols = 0. 0.04253 + 6.00 deg.: X-S = 2.502344 mb/sr, & pols =-0. 0.04917 + 7.00 deg.: X-S = 2.522796 mb/sr, & pols = 0. 0.05487 + 8.00 deg.: X-S = 2.545565 mb/sr, & pols = 0. 0.05956 + 9.00 deg.: X-S = 2.570286 mb/sr, & pols = 0. 0.06317 + 10.00 deg.: X-S = 2.596542 mb/sr, & pols = 0. 0.06566 + 11.00 deg.: X-S = 2.623867 mb/sr, & pols =-0. 0.06702 + 12.00 deg.: X-S = 2.651748 mb/sr, & pols =-0. 0.06726 + 13.00 deg.: X-S = 2.679630 mb/sr, & pols = 0. 0.06639 + 14.00 deg.: X-S = 2.706924 mb/sr, & pols =-0. 0.06447 + 15.00 deg.: X-S = 2.733013 mb/sr, & pols =-0. 0.06154 + 16.00 deg.: X-S = 2.757268 mb/sr, & pols =-0. 0.05767 + 17.00 deg.: X-S = 2.779051 mb/sr, & pols = 0. 0.05291 + 18.00 deg.: X-S = 2.797737 mb/sr, & pols =-0. 0.04734 + 19.00 deg.: X-S = 2.812721 mb/sr, & pols = 0. 0.04103 + 20.00 deg.: X-S = 2.823435 mb/sr, & pols = 0. 0.03406 + 21.00 deg.: X-S = 2.829363 mb/sr, & pols = 0. 0.02647 + 22.00 deg.: X-S = 2.830050 mb/sr, & pols = 0. 0.01835 + 23.00 deg.: X-S = 2.825121 mb/sr, & pols = 0. 0.00976 + 24.00 deg.: X-S = 2.814286 mb/sr, & pols = 0. 0.00074 + 25.00 deg.: X-S = 2.797351 mb/sr, & pols = 0. -0.00864 + 26.00 deg.: X-S = 2.774225 mb/sr, & pols = 0. -0.01834 + 27.00 deg.: X-S = 2.744922 mb/sr, & pols =-0. -0.02829 + 28.00 deg.: X-S = 2.709563 mb/sr, & pols = 0. -0.03845 + 29.00 deg.: X-S = 2.668373 mb/sr, & pols =-0. -0.04876 + 30.00 deg.: X-S = 2.621679 mb/sr, & pols = 0. -0.05918 + 31.00 deg.: X-S = 2.569896 mb/sr, & pols = 0. -0.06962 + 32.00 deg.: X-S = 2.513525 mb/sr, & pols = 0. -0.08004 + 33.00 deg.: X-S = 2.453136 mb/sr, & pols =-0. -0.09034 + 34.00 deg.: X-S = 2.389354 mb/sr, & pols =-0. -0.10046 + 35.00 deg.: X-S = 2.322848 mb/sr, & pols =-0. -0.11029 + 36.00 deg.: X-S = 2.254308 mb/sr, & pols =-0. -0.11973 + 37.00 deg.: X-S = 2.184437 mb/sr, & pols =-0. -0.12867 + 38.00 deg.: X-S = 2.113931 mb/sr, & pols =-0. -0.13699 + 39.00 deg.: X-S = 2.043463 mb/sr, & pols =-0. -0.14454 + 40.00 deg.: X-S = 1.973671 mb/sr, & pols =-0. -0.15120 + 41.00 deg.: X-S = 1.905147 mb/sr, & pols =-0. -0.15682 + 42.00 deg.: X-S = 1.838424 mb/sr, & pols =-0. -0.16127 + 43.00 deg.: X-S = 1.773968 mb/sr, & pols =-0. -0.16444 + 44.00 deg.: X-S = 1.712172 mb/sr, & pols =-0. -0.16620 + 45.00 deg.: X-S = 1.653356 mb/sr, & pols = 0. -0.16648 + 46.00 deg.: X-S = 1.597758 mb/sr, & pols =-0. -0.16524 + 47.00 deg.: X-S = 1.545539 mb/sr, & pols =-0. -0.16245 + 48.00 deg.: X-S = 1.496786 mb/sr, & pols =-0. -0.15817 + 49.00 deg.: X-S = 1.451515 mb/sr, & pols =-0. -0.15246 + 50.00 deg.: X-S = 1.409674 mb/sr, & pols = 0. -0.14547 + 51.00 deg.: X-S = 1.371155 mb/sr, & pols =-0. -0.13738 + 52.00 deg.: X-S = 1.335798 mb/sr, & pols = 0. -0.12840 + 53.00 deg.: X-S = 1.303399 mb/sr, & pols = 0. -0.11880 + 54.00 deg.: X-S = 1.273721 mb/sr, & pols = 0. -0.10885 + 55.00 deg.: X-S = 1.246500 mb/sr, & pols = 0. -0.09885 + 56.00 deg.: X-S = 1.221454 mb/sr, & pols = 0. -0.08910 + 57.00 deg.: X-S = 1.198293 mb/sr, & pols = 0. -0.07987 + 58.00 deg.: X-S = 1.176719 mb/sr, & pols = 0. -0.07144 + 59.00 deg.: X-S = 1.156442 mb/sr, & pols = 0. -0.06404 + 60.00 deg.: X-S = 1.137178 mb/sr, & pols = 0. -0.05788 + 61.00 deg.: X-S = 1.118655 mb/sr, & pols = 0. -0.05313 + 62.00 deg.: X-S = 1.100619 mb/sr, & pols = 0. -0.04990 + 63.00 deg.: X-S = 1.082833 mb/sr, & pols = 0. -0.04828 + 64.00 deg.: X-S = 1.065084 mb/sr, & pols = 0. -0.04829 + 65.00 deg.: X-S = 1.047179 mb/sr, & pols = 0. -0.04994 + 66.00 deg.: X-S = 1.028948 mb/sr, & pols = 0. -0.05319 + 67.00 deg.: X-S = 1.010247 mb/sr, & pols = 0. -0.05794 + 68.00 deg.: X-S = 0.990951 mb/sr, & pols = 0. -0.06408 + 69.00 deg.: X-S = 0.970962 mb/sr, & pols = 0. -0.07146 + 70.00 deg.: X-S = 0.950202 mb/sr, & pols = 0. -0.07992 + 71.00 deg.: X-S = 0.928615 mb/sr, & pols = 0. -0.08924 + 72.00 deg.: X-S = 0.906168 mb/sr, & pols = 0. -0.09921 + 73.00 deg.: X-S = 0.882848 mb/sr, & pols = 0. -0.10958 + 74.00 deg.: X-S = 0.858661 mb/sr, & pols = 0. -0.12010 + 75.00 deg.: X-S = 0.833636 mb/sr, & pols = 0. -0.13049 + 76.00 deg.: X-S = 0.807818 mb/sr, & pols = 0. -0.14047 + 77.00 deg.: X-S = 0.781275 mb/sr, & pols = 0. -0.14975 + 78.00 deg.: X-S = 0.754091 mb/sr, & pols = 0. -0.15803 + 79.00 deg.: X-S = 0.726370 mb/sr, & pols =-0. -0.16500 + 80.00 deg.: X-S = 0.698230 mb/sr, & pols =-0. -0.17035 + 81.00 deg.: X-S = 0.669810 mb/sr, & pols =-0. -0.17379 + 82.00 deg.: X-S = 0.641258 mb/sr, & pols =-0. -0.17500 + 83.00 deg.: X-S = 0.612740 mb/sr, & pols =-0. -0.17371 + 84.00 deg.: X-S = 0.584430 mb/sr, & pols = 0. -0.16964 + 85.00 deg.: X-S = 0.556508 mb/sr, & pols =-0. -0.16255 + 86.00 deg.: X-S = 0.529164 mb/sr, & pols = 0. -0.15224 + 87.00 deg.: X-S = 0.502586 mb/sr, & pols = 0. -0.13858 + 88.00 deg.: X-S = 0.476959 mb/sr, & pols = 0. -0.12149 + 89.00 deg.: X-S = 0.452466 mb/sr, & pols = 0. -0.10104 + 90.00 deg.: X-S = 0.429277 mb/sr, & pols = 0. -0.07738 + 91.00 deg.: X-S = 0.407548 mb/sr, & pols = 0. -0.05087 + 92.00 deg.: X-S = 0.387419 mb/sr, & pols = 0. -0.02202 + 93.00 deg.: X-S = 0.369005 mb/sr, & pols = 0. 0.00846 + 94.00 deg.: X-S = 0.352398 mb/sr, & pols = 0. 0.03964 + 95.00 deg.: X-S = 0.337662 mb/sr, & pols = 0. 0.07045 + 96.00 deg.: X-S = 0.324828 mb/sr, & pols = 0. 0.09967 + 97.00 deg.: X-S = 0.313896 mb/sr, & pols = 0. 0.12606 + 98.00 deg.: X-S = 0.304833 mb/sr, & pols = 0. 0.14842 + 99.00 deg.: X-S = 0.297571 mb/sr, & pols = 0. 0.16572 + 100.00 deg.: X-S = 0.292010 mb/sr, & pols = 0. 0.17719 + 101.00 deg.: X-S = 0.288020 mb/sr, & pols = 0. 0.18239 + 102.00 deg.: X-S = 0.285441 mb/sr, & pols = 0. 0.18126 + 103.00 deg.: X-S = 0.284088 mb/sr, & pols = 0. 0.17409 + 104.00 deg.: X-S = 0.283754 mb/sr, & pols = 0. 0.16148 + 105.00 deg.: X-S = 0.284216 mb/sr, & pols = 0. 0.14427 + 106.00 deg.: X-S = 0.285240 mb/sr, & pols = 0. 0.12342 + 107.00 deg.: X-S = 0.286588 mb/sr, & pols = 0. 0.09997 + 108.00 deg.: X-S = 0.288018 mb/sr, & pols = 0. 0.07495 + 109.00 deg.: X-S = 0.289299 mb/sr, & pols = 0. 0.04931 + 110.00 deg.: X-S = 0.290208 mb/sr, & pols = 0. 0.02393 + 111.00 deg.: X-S = 0.290544 mb/sr, & pols =-0. -0.00042 + 112.00 deg.: X-S = 0.290124 mb/sr, & pols = 0. -0.02309 + 113.00 deg.: X-S = 0.288797 mb/sr, & pols =-0. -0.04351 + 114.00 deg.: X-S = 0.286441 mb/sr, & pols =-0. -0.06118 + 115.00 deg.: X-S = 0.282968 mb/sr, & pols =-0. -0.07569 + 116.00 deg.: X-S = 0.278327 mb/sr, & pols = 0. -0.08665 + 117.00 deg.: X-S = 0.272505 mb/sr, & pols =-0. -0.09371 + 118.00 deg.: X-S = 0.265526 mb/sr, & pols = 0. -0.09653 + 119.00 deg.: X-S = 0.257450 mb/sr, & pols =-0. -0.09479 + 120.00 deg.: X-S = 0.248372 mb/sr, & pols = 0. -0.08814 + 121.00 deg.: X-S = 0.238419 mb/sr, & pols = 0. -0.07624 + 122.00 deg.: X-S = 0.227746 mb/sr, & pols = 0. -0.05875 + 123.00 deg.: X-S = 0.216532 mb/sr, & pols = 0. -0.03535 + 124.00 deg.: X-S = 0.204972 mb/sr, & pols = 0. -0.00576 + 125.00 deg.: X-S = 0.193278 mb/sr, & pols = 0. 0.03021 + 126.00 deg.: X-S = 0.181664 mb/sr, & pols = 0. 0.07257 + 127.00 deg.: X-S = 0.170350 mb/sr, & pols = 0. 0.12108 + 128.00 deg.: X-S = 0.159549 mb/sr, & pols = 0. 0.17511 + 129.00 deg.: X-S = 0.149464 mb/sr, & pols = 0. 0.23352 + 130.00 deg.: X-S = 0.140282 mb/sr, & pols = 0. 0.29447 + 131.00 deg.: X-S = 0.132171 mb/sr, & pols = 0. 0.35539 + 132.00 deg.: X-S = 0.125272 mb/sr, & pols = 0. 0.41297 + 133.00 deg.: X-S = 0.119702 mb/sr, & pols = 0. 0.46339 + 134.00 deg.: X-S = 0.115545 mb/sr, & pols = 0. 0.50271 + 135.00 deg.: X-S = 0.112853 mb/sr, & pols = 0. 0.52754 + 136.00 deg.: X-S = 0.111647 mb/sr, & pols = 0. 0.53570 + 137.00 deg.: X-S = 0.111914 mb/sr, & pols = 0. 0.52665 + 138.00 deg.: X-S = 0.113611 mb/sr, & pols = 0. 0.50174 + 139.00 deg.: X-S = 0.116664 mb/sr, & pols = 0. 0.46381 + 140.00 deg.: X-S = 0.120974 mb/sr, & pols = 0. 0.41671 + 141.00 deg.: X-S = 0.126416 mb/sr, & pols = 0. 0.36452 + 142.00 deg.: X-S = 0.132845 mb/sr, & pols =-0. 0.31108 + 143.00 deg.: X-S = 0.140103 mb/sr, & pols =-0. 0.25956 + 144.00 deg.: X-S = 0.148018 mb/sr, & pols =-0. 0.21234 + 145.00 deg.: X-S = 0.156412 mb/sr, & pols =-0. 0.17105 + 146.00 deg.: X-S = 0.165105 mb/sr, & pols =-0. 0.13670 + 147.00 deg.: X-S = 0.173918 mb/sr, & pols =-0. 0.10977 + 148.00 deg.: X-S = 0.182680 mb/sr, & pols =-0. 0.09041 + 149.00 deg.: X-S = 0.191228 mb/sr, & pols =-0. 0.07851 + 150.00 deg.: X-S = 0.199413 mb/sr, & pols = 0. 0.07381 + 151.00 deg.: X-S = 0.207104 mb/sr, & pols = 0. 0.07596 + 152.00 deg.: X-S = 0.214185 mb/sr, & pols = 0. 0.08454 + 153.00 deg.: X-S = 0.220564 mb/sr, & pols = 0. 0.09907 + 154.00 deg.: X-S = 0.226166 mb/sr, & pols = 0. 0.11908 + 155.00 deg.: X-S = 0.230940 mb/sr, & pols = 0. 0.14402 + 156.00 deg.: X-S = 0.234856 mb/sr, & pols = 0. 0.17333 + 157.00 deg.: X-S = 0.237903 mb/sr, & pols = 0. 0.20640 + 158.00 deg.: X-S = 0.240090 mb/sr, & pols = 0. 0.24256 + 159.00 deg.: X-S = 0.241446 mb/sr, & pols = 0. 0.28107 + 160.00 deg.: X-S = 0.242011 mb/sr, & pols = 0. 0.32113 + 161.00 deg.: X-S = 0.241842 mb/sr, & pols = 0. 0.36184 + 162.00 deg.: X-S = 0.241008 mb/sr, & pols = 0. 0.40222 + 163.00 deg.: X-S = 0.239583 mb/sr, & pols = 0. 0.44120 + 164.00 deg.: X-S = 0.237651 mb/sr, & pols = 0. 0.47767 + 165.00 deg.: X-S = 0.235298 mb/sr, & pols = 0. 0.51040 + 166.00 deg.: X-S = 0.232614 mb/sr, & pols = 0. 0.53818 + 167.00 deg.: X-S = 0.229686 mb/sr, & pols = 0. 0.55975 + 168.00 deg.: X-S = 0.226601 mb/sr, & pols = 0. 0.57391 + 169.00 deg.: X-S = 0.223444 mb/sr, & pols = 0. 0.57954 + 170.00 deg.: X-S = 0.220291 mb/sr, & pols =-0. 0.57565 + 171.00 deg.: X-S = 0.217217 mb/sr, & pols =-0. 0.56147 + 172.00 deg.: X-S = 0.214289 mb/sr, & pols =-0. 0.53646 + 173.00 deg.: X-S = 0.211566 mb/sr, & pols =-0. 0.50041 + 174.00 deg.: X-S = 0.209101 mb/sr, & pols =-0. 0.45348 + 175.00 deg.: X-S = 0.206939 mb/sr, & pols =-0. 0.39620 + 176.00 deg.: X-S = 0.205119 mb/sr, & pols =-0. 0.32950 + 177.00 deg.: X-S = 0.203672 mb/sr, & pols =-0. 0.25473 + 178.00 deg.: X-S = 0.202621 mb/sr, & pols =-0. 0.17354 + 179.00 deg.: X-S = 0.201984 mb/sr, & pols =-0. 0.08791 + 180.00 deg.: X-S = 0.201770 mb/sr, & pols =-0. 0.00000 + Integrated 1.0187E+01 mb, over [ 0.000, 180.000] at 25.0000 MeV + Finished all xsecs @ 1.82150006E-02 + + The following files have been created: + 3:local copy of User input. 6:standard output. + 7:elastic S-matrix elements. 13:total cross sections/state. + 16:tables of cross sections. 35:Astrophysics S-factors / Ecm. + 38:cross sections for each J/pi. 39:cross sections for each Ecm. + 40:all cross sectns. for each Elab. 45:scat phase shift as E functions. + 56:Fusion for each Jtotal. 75:S-factors for lab energies. + 201:Separate cross sections. 202:Separate cross sections. + + PARAMETERS : MAXQRN MLOC LMAX1 MCLIST MFNL MPWCOUP + ALLOWED : 1 38 60 36 4 0 + REQUIRED: 0 9 42 4 0 0 + + + ACCURACY ANALYSIS at 25.000 MeV : + + Elastic h*k = 0.021 so OK compared with 0.200 + + Real(S-el) > 0.01 first at J = 1.5 + Real(S-el) > 0.10 first at J = 1.5 + Real(S-el) > 0.50 first at J = 5.5 + Real(S-el) > 0.90 first at J = 6.5 + Real(S-el) > 0.99 first at J = 8.5 + R-turn = 38.39 fm at J = 40.5 + + Forward-angle excitation cut off below 1.761 deg by max JT = 40.5 + and below 3.369 deg by max R. + + Total CPU 0 time = 0.02 seconds diff --git a/tests/regression/frescox/outputs/Ca48_pn_IAS_35MeV.out b/tests/regression/frescox/outputs/Ca48_pn_IAS_35MeV.out new file mode 100644 index 00000000..32f648c3 --- /dev/null +++ b/tests/regression/frescox/outputs/Ca48_pn_IAS_35MeV.out @@ -0,0 +1,1300 @@ +Running on kyle-ThinkPad-X390 + FRESCOX - version 7.2-20-ga7f491: Coupled Reaction Channels on gfortran + + Using NAMELIST input + + 48Ca(p,n)48Sc(IAS) DWBA + + 0.020 20.000 0.500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + + Centre-of-mass Range is 1002 * 0.0200 fm., Maximum at 20.00 fm., Interpolating NL forms every 0.50 fm. + Non - locality width is 4 * 0.0200 fm., Maximum of 0.06 fm., Centred at 0.00 fm. + 2-Nucleon Separation of 0 * 0.5000 fm., Maximum of 0.00 fm., Minimum at 0.00 fm. + Maximum single particle bins of 20.0000 fm. + M,Mint = 1001 1001 + + + Range of total J is 0.0 <= J <= 40.0 (at least 0.0) and Absorbtion => -1.0000 mb. + Dry Run = F, CC set limits = 0 0, Relativistic kinematics = , Both/Far/Near Analyses = 1 + + Cross Sections (and up to T1 for 0=projectile) for Theta from 0.0 to 180.0 in steps of 1.0 degrees, DGAM=0, grace=T, Coordinates = 0 (Mads) + + Lower Radial Cutoff = maximum of -1.60*L*h & 0.0 fm., Lower Cutoff for Couplings = 0.0 fm. + + + Iterate Couplings between 0 and 1 times, to 0.000 % if sooner. + Block solved exactly = 0 chs., with Pade = 0 & Isocen = =0, NOSOL = F, CCREAL = F, initwf = 0 + Small channels are 0.00E+00 and small couplings are 1.00E-12 of unitarity + + NL quadrature with 18 Gaussian points, Calculate multipoles up to 50 from 0 + M-transfers for lp+lt greater than or equal to 6, Angular Integration Cutoff below 2.7778 % + + + Trace switches are : CHANS = 1, LISTCC = 0, TRENEG = 0, CDETR = 0, SMATS = 2, XSTABL = 3, NLPL = 0 + + WAVES = 0, LAMPL = 0, VEFF = 0, KFUS = 0, WDISK = 0, BPM = 0, MELFIL = 0 + + CDCC = 0, NFUS = 0, TCFILE = 0 + + Using unit mass = 1.000000 amu and 1/fine-structure constant = 137.03599 ( 1.000000 * true ) with hc = 197.32705 MeV.fm, + thus 2*amu/hbar^2 = 0.0478450 = 1/20.9008 and Coulomb constant = 0.1574855 so e^2 = 1.43996515, and nuclear magneton= 0.1261183 + + Now pre-scan input and save to file 3 + + + + *********** PARTITION NUMBER 1 ****************************************************************************************** + + PROJ=p MASS= 1.0000 Z= 1.0, # STATES= 1T, TARG=48Ca MASS= 48.0000 Z= 20.0, Q-VALUE = 0.0000 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 0.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 1 J= 0.0+ (B# 1), E= 0.0000, K= 0.0 + + + *********** PARTITION NUMBER 2 ****************************************************************************************** + + PROJ=n MASS= 1.0000 Z= 0.0, # STATES= 1T, TARG=48Sc MASS= 48.0000 Z= 21.0, Q-VALUE = -0.5000 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 0.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 2 J= 0.0+ (B# 1), E= 6.6770, K= 0.0 + + + ************************************************************************************************************************************ + + The following POTENTIALS are defined : + + KP# TYPE IT SHAPE at V1 r1 a1 V2 r2 a2 A-in A-used + + + A#1 A#2 r0c ac h @ 1 + 1 0=Coulomb 0=CHARGE (WS) 1 48.000 0.000 1.2713 0.0000 0.0000 0.0000 0.000 48.000 0.02000 + + 1 1=Volume 0=Woods-Saxon 2 47.1355 1.1923 0.6707 3.5384 1.1923 0.6707 0.000 48.000 + + 1 2=Surface 0=Woods-Saxon 3 0.0000 0.0000 0.0000 6.8545 1.2848 0.5437 0.000 48.000 + + 1 3=Projtl S.O. 0=Woods-Saxon 4 5.1126 1.0074 0.5900 -0.2065 1.0074 0.5900 0.000 48.000 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 2 + 2 0=Coulomb 0=CHARGE (WS) 5 48.000 0.000 1.0000 0.0000 0.0000 0.0000 0.000 48.000 0.02000 + + 2 1=Volume 0=Woods-Saxon 6 42.0238 1.1923 0.6707 2.4923 1.1923 0.6707 0.000 48.000 + + 2 2=Surface 0=Woods-Saxon 7 0.0000 0.0000 0.0000 5.6317 1.2848 0.5367 0.000 48.000 + + 2 3=Projtl S.O. 0=Woods-Saxon 8 5.2172 1.0074 0.5900 -0.1629 1.0074 0.5900 0.000 48.000 + + ************************************************************************************************************************************ + + TWO-way COUPLING # 1 for partitions 2 <- 1 of KIND 1, 0 2 0 -1 -1 & P1,P2 = 1.0000 1.0000 : for J <= 40.5 & R < 19.9 fm. + + General projectile/target multipole+spin transfers + Therefore from file 4 read LOCAL form factor of COMPLEX elements, and use as given. with Re,Im scalings of 1.0000 1.0000 + + Read 1001 point form factor at h = 0.020 fm from 0.000:Lane U1 central + Scaled by 5.0133 for L-transfer 0, projectile transfer 0.0, and target transfer = 0.0 to excited pair 1 from pair 1 + Angular momentum operator itself: -1 on wf derivative: -1 + + + Incoming partition 1 in excitation state # 1, Laboratory Energy given for partition 1 Nucleus 1 in Excitation pair 1 + +0Lab. ENERGY ranges : + + from 35.0000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + + Largest real,imaginary parts of any form factor at R= 19.80 are 7.27E-02 3.41E-10 MeV + Finished all Couplings @ 1.71000010E-03 + + Symmetric Hamiltonian +1*********************************************************************************************************************************** +************************************************************************************************************************************ + + INCOMING p ; LABORATORY p ENERGY = 35.000 MeV. + + *********************************************************************************************************************************** + *********************************************************************************************************************************** + + Allocate arrays for 2 channels, of which 1 need wfs. + + ######################################################################################################################### + # # + # Total SPIN and PARITY = 0.5 +, 2 channels, 0 in 1st block. Rmin & Coul turning = 0.0 8.400E-01 fm. # + # # + ######################################################################################################################### + + + + C Projectl Target # EX. (L Proj) J + Targ = Jtotal E-cm Re K Re Eta RM*K CH G-REL + 1 p 48Ca # 1: I 0 0.5 0.5 0.0 0.5 34.28571 1.26765 0.53240 25.3529 0.43135 -0.26334 1 1 1 1 1.00000 + 2 n 48Sc # 1: 0 0.5 0.5 0.0 0.5 27.10871 1.12719 0.00000 22.5437 0.26244 -0.42559 2 1 2 1 1.00000 + S-matrix 1 = -0.19533 0.36826 for L= 0, J= 0.5 channel on core I = 0.0 from L= 0, Acc. loss = 0.0 D. + Elastic phase shift 1 = 58.971 25.067 deg. for the L = 0, J = 0.5 channel. + 0.5 -0.19532901 0.36826480i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 0.5+/ 0 @ 1 = 16.153 , Out: 0.000# 0.262 15.891f + + + Total SPIN, PARITY = 0.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.0 0.8 + S-matrix 1 = 0.31167 0.24430 for L= 1, J= 0.5 channel on core I = 0.0 from L= 1, Acc. loss = 0.0 D. + Elastic phase shift 1 = 19.045 26.537 deg. for the L = 1, J = 0.5 channel. + 0.5 0.31167342 0.24429978i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 0.5-/ 1 @ 1 = 16.484 , Out: 0.000# 0.259 16.226f + + + Total SPIN, PARITY = 1.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.0 1.6 + S-matrix 1 = 0.40758 -0.04171 for L= 2, J= 1.5 channel on core I = 0.0 from L= 2, Acc. loss = 0.0 D. + Elastic phase shift 1 = -2.922 25.562 deg. for the L = 2, J = 1.5 channel. + 1.5 0.40758478 -0.04171150i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 1.5+/ 2 @ 1 = 32.537 , Out: 0.000# 0.458 32.079f + + + Total SPIN, PARITY = 1.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.0 1.6 + S-matrix 1 = 0.23315 0.32289 for L= 1, J= 1.5 channel on core I = 0.0 from L= 1, Acc. loss = 0.0 D. + Elastic phase shift 1 = 27.084 26.374 deg. for the L = 1, J = 1.5 channel. + 1.5 0.23314804 0.32289084i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 1.5-/ 1 @ 1 = 32.899 , Out: 0.000# 0.523 32.376f + + + Total SPIN, PARITY = 2.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.1 2.4 + S-matrix 1 = 0.41391 0.14811 for L= 2, J= 2.5 channel on core I = 0.0 from L= 2, Acc. loss = 0.0 D. + Elastic phase shift 1 = 9.845 23.545 deg. for the L = 2, J = 2.5 channel. + 2.5 0.41390581 0.14811430i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 2.5+/ 2 @ 1 = 47.316 , Out: 0.000# 0.711 46.605f + + + Total SPIN, PARITY = 2.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.1 2.4 + S-matrix 1 = 0.13852 -0.30707 for L= 3, J= 2.5 channel on core I = 0.0 from L= 3, Acc. loss = 0.0 D. + Elastic phase shift 1 = -32.860 31.171 deg. for the L = 3, J = 2.5 channel. + 2.5 0.13851726 -0.30706558i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 2.5-/ 3 @ 1 = 51.996 , Out: 0.000# 0.649 51.347f + + + Total SPIN, PARITY = 3.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.1 3.2 + S-matrix 1 = -0.23321 -0.34596 for L= 4, J= 3.5 channel on core I = 0.0 from L= 4, Acc. loss = 0.0 D. + Elastic phase shift 1 = -61.992 25.042 deg. for the L = 4, J = 3.5 channel. + 3.5 -0.23320895 -0.34595695i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 3.5+/ 4 @ 1 = 64.589 , Out: 0.000# 0.638 63.951f + + + Total SPIN, PARITY = 3.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.1 3.2 + S-matrix 1 = 0.33711 -0.11274 for L= 3, J= 3.5 channel on core I = 0.0 from L= 3, Acc. loss = 0.0 D. + Elastic phase shift 1 = -9.246 29.631 deg. for the L = 3, J = 3.5 channel. + 3.5 0.33711220 -0.11273763i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 3.5-/ 3 @ 1 = 68.320 , Out: 0.000# 0.873 67.448f + + + Total SPIN, PARITY = 4.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.1 4.0 + S-matrix 1 = 0.16056 -0.41163 for L= 4, J= 4.5 channel on core I = 0.0 from L= 4, Acc. loss = 0.0 D. + Elastic phase shift 1 = -34.346 23.400 deg. for the L = 4, J = 4.5 channel. + 4.5 0.16056047 -0.41162523i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 4.5+/ 4 @ 1 = 78.669 , Out: 0.000# 0.851 77.819f + + + Total SPIN, PARITY = 4.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.1 4.0 + S-matrix 1 = -0.16832 -0.00235 for L= 5, J= 4.5 channel on core I = 0.0 from L= 5, Acc. loss = 0.0 D. + Elastic phase shift 1 = -89.601 51.045 deg. for the L = 5, J = 4.5 channel. + 4.5 -0.16831905 -0.00234612i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 4.5-/ 5 @ 1 = 94.982 , Out: 0.000# 0.556 94.425f + + + Total SPIN, PARITY = 5.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 4.8 + S-matrix 1 = 0.32667 0.24790 for L= 6, J= 5.5 channel on core I = 0.0 from L= 6, Acc. loss = 0.0 D. + Elastic phase shift 1 = 18.597 25.536 deg. for the L = 6, J = 5.5 channel. + 5.5 0.32667190 0.24790303i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 5.5+/ 6 @ 1 = 97.575 , Out: 0.000# 0.280 97.296f + + + Total SPIN, PARITY = 5.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 4.8 + S-matrix 1 = -0.06443 -0.41275 for L= 5, J= 5.5 channel on core I = 0.0 from L= 5, Acc. loss = 0.0 D. + Elastic phase shift 1 = -49.436 25.006 deg. for the L = 5, J = 5.5 channel. + 5.5 -0.06443425 -0.41274647i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 5.5-/ 5 @ 1 = 96.832 , Out: 0.000# 0.798 96.033f + + + Total SPIN, PARITY = 6.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 5.5 + S-matrix 1 = 0.06888 0.10799 for L= 6, J= 6.5 channel on core I = 0.0 from L= 6, Acc. loss = 0.0 D. + Elastic phase shift 1 = 28.734 58.872 deg. for the L = 6, J = 6.5 channel. + 6.5 0.06888396 0.10799344i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 6.5+/ 6 @ 1 = 134.607 , Out: 0.000# 0.633 133.974f + + + Total SPIN, PARITY = 6.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 5.5 + S-matrix 1 = 0.75877 0.19806 for L= 7, J= 6.5 channel on core I = 0.0 from L= 7, Acc. loss = 0.0 D. + Elastic phase shift 1 = 7.315 6.964 deg. for the L = 7, J = 6.5 channel. + 6.5 0.75876900 0.19805869i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 6.5-/ 7 @ 1 = 52.694 , Out: 0.000# 0.052 52.642f + + + Total SPIN, PARITY = 7.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 6.3 + S-matrix 1 = 0.93105 0.08896 for L= 8, J= 7.5 channel on core I = 0.0 from L= 8, Acc. loss = 0.0 D. + Elastic phase shift 1 = 2.729 1.916 deg. for the L = 8, J = 7.5 channel. + 7.5 0.93105015 0.08896190i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 7.5+/ 8 @ 1 = 19.587 , Out: 0.000# 5.833/ 19.581f + + + Total SPIN, PARITY = 7.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 6.3 + S-matrix 1 = 0.67333 0.25954 for L= 7, J= 7.5 channel on core I = 0.0 from L= 7, Acc. loss = 0.0 D. + Elastic phase shift 1 = 10.540 9.347 deg. for the L = 7, J = 7.5 channel. + 7.5 0.67332564 0.25954064i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 7.5-/ 7 @ 1 = 74.959 , Out: 0.000# 0.097 74.863f + + + Total SPIN, PARITY = 8.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.2 7.1 + S-matrix 1 = 0.92086 0.11536 for L= 8, J= 8.5 channel on core I = 0.0 from L= 8, Acc. loss = 0.0 D. + Elastic phase shift 1 = 3.570 2.139 deg. for the L = 8, J = 8.5 channel. + 8.5 0.92085617 0.11535709i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 8.5+/ 8 @ 1 = 24.408 , Out: 0.000# 8.063/ 24.400f + + + Total SPIN, PARITY = 8.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.2 7.1 + S-matrix 1 = 0.98102 0.03326 for L= 9, J= 8.5 channel on core I = 0.0 from L= 9, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.971 0.533 deg. for the L = 9, J = 8.5 channel. + 8.5 0.98101561 0.03325778i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 8.5-/ 9 @ 1 = 6.423 , Out: 0.000# 0.583/ 6.422f + + + Total SPIN, PARITY = 9.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.3 7.9 + S-matrix 1 = 0.99473 0.01175 for L= 10, J= 9.5 channel on core I = 0.0 from L= 10, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.338 0.149 deg. for the L = 10, J = 9.5 channel. + 9.5 0.99472737 0.01174593i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 9.5+/10 @ 1 = 2.029 , Out: 0.000# 0.058/ 2.029f + + + Total SPIN, PARITY = 9.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.3 7.9 + S-matrix 1 = 0.98008 0.04085 for L= 9, J= 9.5 channel on core I = 0.0 from L= 9, Acc. loss = 0.0 D. + Elastic phase shift 1 = 1.193 0.552 deg. for the L = 9, J = 9.5 channel. + 9.5 0.98007925 0.04085404i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 9.5-/ 9 @ 1 = 7.385 , Out: 0.000# 0.700/ 7.385f + + + Total SPIN, PARITY = 10.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.3 8.7 + S-matrix 1 = 0.99468 0.01390 for L= 10, J= 10.5 channel on core I = 0.0 from L= 10, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.400 0.150 deg. for the L = 10, J = 10.5 channel. + 10.5 0.99467771 0.01390428i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 10.5+/10 @ 1 = 2.241 , Out: 0.000# 0.065/ 2.241f + + + Total SPIN, PARITY = 10.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.3 8.7 + S-matrix 1 = 0.99852 0.00407 for L= 11, J= 10.5 channel on core I = 0.0 from L= 11, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.117 0.042 deg. for the L = 11, J = 10.5 channel. + 10.5 0.99851752 0.00406712i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 10.5-/11 @ 1 = 0.634 , Out: 0.000# 5.721# 0.634f + + + Total SPIN, PARITY = 11.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.3 9.5 + S-matrix 1 = 0.99958 0.00140 for L= 12, J= 11.5 channel on core I = 0.0 from L= 12, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.040 0.012 deg. for the L = 12, J = 11.5 channel. + 11.5 0.99957861 0.00139620i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 11.5+/12 @ 1 = 0.197 , Out: 0.000# 0.575# 0.197f + + + Total SPIN, PARITY = 11.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.3 9.5 + S-matrix 1 = 0.99853 0.00469 for L= 11, J= 11.5 channel on core I = 0.0 from L= 11, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.135 0.042 deg. for the L = 11, J = 11.5 channel. + 11.5 0.99852769 0.00469283i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 11.5-/11 @ 1 = 0.685 , Out: 0.000# 6.306# 0.685f + + + Total SPIN, PARITY = 12.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.4 10.3 + S-matrix 1 = 0.99958 0.00158 for L= 12, J= 12.5 channel on core I = 0.0 from L= 12, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.045 0.012 deg. for the L = 12, J = 12.5 channel. + 12.5 0.99958436 0.00157992i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 12.5+/12 @ 1 = 0.211 , Out: 0.000# 0.625# 0.211f + + + Total SPIN, PARITY = 12.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.4 10.3 + S-matrix 1 = 0.99988 0.00048 for L= 13, J= 12.5 channel on core I = 0.0 from L= 13, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.014 0.003 deg. for the L = 13, J = 12.5 channel. + 12.5 0.99987898 0.00047693i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 12.5-/13 @ 1 = 0.061 , Out: 0.000# 0.058# 0.061f + + + Total SPIN, PARITY = 13.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.4 11.1 + S-matrix 1 = 0.99996 0.00016 for L= 14, J= 13.5 channel on core I = 0.0 from L= 14, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.005 0.001 deg. for the L = 14, J = 13.5 channel. + 13.5 0.99996488 0.00016233i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 13.5+/14 @ 1 = 0.019 , Out: 0.000# 0.006# 0.019f + + + Total SPIN, PARITY = 13.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.4 11.1 + S-matrix 1 = 0.99988 0.00053 for L= 13, J= 13.5 channel on core I = 0.0 from L= 13, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.015 0.003 deg. for the L = 13, J = 13.5 channel. + 13.5 0.99988099 0.00053115i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 13.5-/13 @ 1 = 0.065 , Out: 0.000# 0.063# 0.065f + + + Total SPIN, PARITY = 14.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.4 11.9 + S-matrix 1 = 0.99997 0.00018 for L= 14, J= 14.5 channel on core I = 0.0 from L= 14, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.005 0.001 deg. for the L = 14, J = 14.5 channel. + 14.5 0.99996550 0.00017835i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 14.5+/14 @ 1 = 0.020 , Out: 0.000# 0.006# 0.020f + + + Total SPIN, PARITY = 14.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.4 11.9 + S-matrix 1 = 0.99999 0.00006 for L= 15, J= 14.5 channel on core I = 0.0 from L= 15, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.002 0.000 deg. for the L = 15, J = 14.5 channel. + 14.5 0.99998970 0.00005509i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 14.5-/15 @ 1 = 6.043/, Out: 0.000# 0.001# 0.006f + + + Total SPIN, PARITY = 15.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.5 12.6 + S-matrix 1 = 1.00000 0.00002 for L= 16, J= 15.5 channel on core I = 0.0 from L= 16, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.001 0.000 deg. for the L = 16, J = 15.5 channel. + 15.5 0.99999694 0.00001865i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 15.5+/16 @ 1 = 1.913/, Out: 0.000# 0.000# 0.002f + + + Total SPIN, PARITY = 15.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.5 12.6 + S-matrix 1 = 0.99999 0.00006 for L= 15, J= 15.5 channel on core I = 0.0 from L= 15, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.002 0.000 deg. for the L = 15, J = 15.5 channel. + 15.5 0.99998988 0.00005982i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 15.5-/15 @ 1 = 6.327/, Out: 0.000# 0.001# 0.006f + + + Total SPIN, PARITY = 16.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.5 13.4 + S-matrix 1 = 1.00000 0.00002 for L= 16, J= 16.5 channel on core I = 0.0 from L= 16, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.001 0.000 deg. for the L = 16, J = 16.5 channel. + 16.5 0.99999700 0.00002004i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 16.5+/16 @ 1 = 1.995/, Out: 0.000# 0.000# 0.002f + + + Total SPIN, PARITY = 16.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.5 13.4 + S-matrix 1 = 1.00000 0.00001 for L= 17, J= 16.5 channel on core I = 0.0 from L= 17, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 17, J = 16.5 channel. + 16.5 0.99999908 0.00000630i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 16.5-/17 @ 1 = 0.610/, Out: 0.000# 0.000# 0.001f + + + Total SPIN, PARITY = 17.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.5 14.2 + S-matrix 1 = 1.00000 0.00000 for L= 18, J= 17.5 channel on core I = 0.0 from L= 18, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 18, J = 17.5 channel. + 17.5 0.99999972 0.00000212i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 17.5+/18 @ 1 = 0.196/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 17.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.5 14.2 + S-matrix 1 = 1.00000 0.00001 for L= 17, J= 17.5 channel on core I = 0.0 from L= 17, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 17, J = 17.5 channel. + 17.5 0.99999910 0.00000671i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 17.5-/17 @ 1 = 0.634/, Out: 0.000# 0.000# 0.001f + + + Total SPIN, PARITY = 18.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.6 15.0 + S-matrix 1 = 1.00000 0.00000 for L= 18, J= 18.5 channel on core I = 0.0 from L= 18, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 18, J = 18.5 channel. + 18.5 0.99999973 0.00000224i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 18.5+/18 @ 1 = 0.203/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 18.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.6 15.0 + S-matrix 1 = 1.00000 0.00000 for L= 19, J= 18.5 channel on core I = 0.0 from L= 19, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 19, J = 18.5 channel. + 18.5 0.99999991 0.00000071i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 18.5-/19 @ 1 = 0.064/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 19.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.6 15.8 + S-matrix 1 = 1.00000 0.00000 for L= 20, J= 19.5 channel on core I = 0.0 from L= 20, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 20, J = 19.5 channel. + 19.5 0.99999997 0.00000024i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 19.5+/20 @ 1 = 0.021/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 19.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.6 15.8 + S-matrix 1 = 1.00000 0.00000 for L= 19, J= 19.5 channel on core I = 0.0 from L= 19, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 19, J = 19.5 channel. + 19.5 0.99999992 0.00000075i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 19.5-/19 @ 1 = 0.066/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 20.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.6 16.6 + S-matrix 1 = 1.00000 0.00000 for L= 20, J= 20.5 channel on core I = 0.0 from L= 20, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 20, J = 20.5 channel. + 20.5 0.99999997 0.00000025i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 20.5+/20 @ 1 = 0.021/, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 20.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.6 16.6 + S-matrix 1 = 1.00000 0.00000 for L= 21, J= 20.5 channel on core I = 0.0 from L= 21, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 21, J = 20.5 channel. + 20.5 0.99999999 0.00000008i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 20.5-/21 @ 1 = 6.831#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 21.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.7 17.4 + S-matrix 1 = 1.00000 0.00000 for L= 22, J= 21.5 channel on core I = 0.0 from L= 22, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 22, J = 21.5 channel. + 21.5 1.00000000 0.00000003i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 21.5+/22 @ 1 = 2.244#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 21.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.7 17.4 + S-matrix 1 = 1.00000 0.00000 for L= 21, J= 21.5 channel on core I = 0.0 from L= 21, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 21, J = 21.5 channel. + 21.5 0.99999999 0.00000008i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 21.5-/21 @ 1 = 7.051#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 22.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.7 18.2 + S-matrix 1 = 1.00000 0.00000 for L= 22, J= 22.5 channel on core I = 0.0 from L= 22, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 22, J = 22.5 channel. + 22.5 1.00000000 0.00000003i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 22.5+/22 @ 1 = 2.314#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 22.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.7 18.2 + S-matrix 1 = 1.00000 0.00000 for L= 23, J= 22.5 channel on core I = 0.0 from L= 23, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 23, J = 22.5 channel. + 22.5 1.00000000 0.00000001i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 22.5-/23 @ 1 = 0.730#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 23.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.7 19.0 + S-matrix 1 = 1.00000 0.00000 for L= 24, J= 23.5 channel on core I = 0.0 from L= 24, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 24, J = 23.5 channel. + 23.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 23.5+/24 @ 1 = 0.232#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 23.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.7 19.0 + S-matrix 1 = 1.00000 0.00000 for L= 23, J= 23.5 channel on core I = 0.0 from L= 23, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 23, J = 23.5 channel. + 23.5 1.00000000 0.00000001i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 23.5-/23 @ 1 = 0.752#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 24.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.8 19.7 + S-matrix 1 = 1.00000 0.00000 for L= 24, J= 24.5 channel on core I = 0.0 from L= 24, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 24, J = 24.5 channel. + 24.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 24.5+/24 @ 1 = 0.238#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 24.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.8 19.7 + S-matrix 1 = 1.00000 0.00000 for L= 25, J= 24.5 channel on core I = 0.0 from L= 25, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 25, J = 24.5 channel. + 24.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 24.5-/25 @ 1 = 0.071#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 25.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.8 20.5 + S-matrix 1 = 1.00000 0.00000 for L= 26, J= 25.5 channel on core I = 0.0 from L= 26, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 26, J = 25.5 channel. + 25.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 25.5+/26 @ 1 = 0.020#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 25.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.8 20.5 + S-matrix 1 = 1.00000 0.00000 for L= 25, J= 25.5 channel on core I = 0.0 from L= 25, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 25, J = 25.5 channel. + 25.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 25.5-/25 @ 1 = 0.073#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 26.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.8 21.3 + S-matrix 1 = 1.00000 0.00000 for L= 26, J= 26.5 channel on core I = 0.0 from L= 26, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 26, J = 26.5 channel. + 26.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 26.5+/26 @ 1 = 0.021#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 26.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.8 21.3 + S-matrix 1 = 1.00000 0.00000 for L= 27, J= 26.5 channel on core I = 0.0 from L= 27, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 27, J = 26.5 channel. + 26.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 26.5-/27 @ 1 = 0.006#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 27.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.9 22.1 + S-matrix 1 = 1.00000 0.00000 for L= 28, J= 27.5 channel on core I = 0.0 from L= 28, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 28, J = 27.5 channel. + 27.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 27.5+/28 @ 1 = 0.001#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 27.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.9 22.1 + S-matrix 1 = 1.00000 0.00000 for L= 27, J= 27.5 channel on core I = 0.0 from L= 27, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 27, J = 27.5 channel. + 27.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 27.5-/27 @ 1 = 0.006#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 28.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.9 22.9 + S-matrix 1 = 1.00000 0.00000 for L= 28, J= 28.5 channel on core I = 0.0 from L= 28, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 28, J = 28.5 channel. + 28.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 28.5+/28 @ 1 = 0.001#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 28.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.9 22.9 + S-matrix 1 = 1.00000 0.00000 for L= 29, J= 28.5 channel on core I = 0.0 from L= 29, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 29, J = 28.5 channel. + 28.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 28.5-/29 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 29.5 +, 2 chs, 0 cc. Rmin & Coul turning = 0.9 23.7 + S-matrix 1 = 1.00000 0.00000 for L= 30, J= 29.5 channel on core I = 0.0 from L= 30, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 30, J = 29.5 channel. + 29.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 29.5+/30 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 29.5 -, 2 chs, 0 cc. Rmin & Coul turning = 0.9 23.7 + S-matrix 1 = 1.00000 0.00000 for L= 29, J= 29.5 channel on core I = 0.0 from L= 29, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 29, J = 29.5 channel. + 29.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 29.5-/29 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 30.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 24.5 + S-matrix 1 = 1.00000 0.00000 for L= 30, J= 30.5 channel on core I = 0.0 from L= 30, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 30, J = 30.5 channel. + 30.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 30.5+/30 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 30.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 24.5 + S-matrix 1 = 1.00000 0.00000 for L= 31, J= 30.5 channel on core I = 0.0 from L= 31, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 31, J = 30.5 channel. + 30.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 30.5-/31 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 31.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 25.3 + S-matrix 1 = 1.00000 0.00000 for L= 32, J= 31.5 channel on core I = 0.0 from L= 32, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 32, J = 31.5 channel. + 31.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 31.5+/32 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 31.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 25.3 + S-matrix 1 = 1.00000 0.00000 for L= 31, J= 31.5 channel on core I = 0.0 from L= 31, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 31, J = 31.5 channel. + 31.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 31.5-/31 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 32.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 26.1 + S-matrix 1 = 1.00000 0.00000 for L= 32, J= 32.5 channel on core I = 0.0 from L= 32, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 32, J = 32.5 channel. + 32.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 32.5+/32 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 32.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 26.1 + S-matrix 1 = 1.00000 0.00000 for L= 33, J= 32.5 channel on core I = 0.0 from L= 33, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 33, J = 32.5 channel. + 32.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 32.5-/33 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 33.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.0 26.8 + S-matrix 1 = 1.00000 0.00000 for L= 34, J= 33.5 channel on core I = 0.0 from L= 34, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 34, J = 33.5 channel. + 33.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 33.5+/34 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 33.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.0 26.8 + S-matrix 1 = 1.00000 0.00000 for L= 33, J= 33.5 channel on core I = 0.0 from L= 33, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 33, J = 33.5 channel. + 33.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 33.5-/33 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 34.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.1 27.6 + S-matrix 1 = 1.00000 0.00000 for L= 34, J= 34.5 channel on core I = 0.0 from L= 34, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 34, J = 34.5 channel. + 34.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 34.5+/34 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 34.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.1 27.6 + S-matrix 1 = 1.00000 0.00000 for L= 35, J= 34.5 channel on core I = 0.0 from L= 35, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 35, J = 34.5 channel. + 34.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 34.5-/35 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 35.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.1 28.4 + S-matrix 1 = 1.00000 0.00000 for L= 36, J= 35.5 channel on core I = 0.0 from L= 36, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 36, J = 35.5 channel. + 35.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 35.5+/36 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 35.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.1 28.4 + S-matrix 1 = 1.00000 0.00000 for L= 35, J= 35.5 channel on core I = 0.0 from L= 35, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 35, J = 35.5 channel. + 35.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 35.5-/35 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 36.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.1 29.2 + S-matrix 1 = 1.00000 0.00000 for L= 36, J= 36.5 channel on core I = 0.0 from L= 36, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 36, J = 36.5 channel. + 36.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 36.5+/36 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 36.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.1 29.2 + S-matrix 1 = 1.00000 0.00000 for L= 37, J= 36.5 channel on core I = 0.0 from L= 37, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 0.000 deg. for the L = 37, J = 36.5 channel. + 36.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 36.5-/37 @ 1 = 0.000#, Out: 0.000# 0.000# 0.000f + + + Total SPIN, PARITY = 37.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.2 30.0 + S-matrix 1 = 1.00000 0.00000 for L= 38, J= 37.5 channel on core I = 0.0 from L= 38, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 38, J = 37.5 channel. + 37.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 37.5+/38 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 37.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.2 30.0 + S-matrix 1 = 1.00000 -0.00000 for L= 37, J= 37.5 channel on core I = 0.0 from L= 37, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 37, J = 37.5 channel. + 37.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 37.5-/37 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 38.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.2 30.8 + S-matrix 1 = 1.00000 -0.00000 for L= 38, J= 38.5 channel on core I = 0.0 from L= 38, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 38, J = 38.5 channel. + 38.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 38.5+/38 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 38.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.2 30.8 + S-matrix 1 = 1.00000 -0.00000 for L= 39, J= 38.5 channel on core I = 0.0 from L= 39, Acc. loss = 0.0 D. + Elastic phase shift 1 = -0.000 -0.000 deg. for the L = 39, J = 38.5 channel. + 38.5 1.00000000 -0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 38.5-/39 @ 1 = -0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 39.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.2 31.6 + S-matrix 1 = 1.00000 0.00000 for L= 40, J= 39.5 channel on core I = 0.0 from L= 40, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 40, J = 39.5 channel. + 39.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 39.5+/40 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 39.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.2 31.6 + S-matrix 1 = 1.00000 0.00000 for L= 39, J= 39.5 channel on core I = 0.0 from L= 39, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 39, J = 39.5 channel. + 39.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 39.5-/39 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 40.5 +, 2 chs, 0 cc. Rmin & Coul turning = 1.3 32.4 + S-matrix 1 = 1.00000 0.00000 for L= 40, J= 40.5 channel on core I = 0.0 from L= 40, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 40, J = 40.5 channel. + 40.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 40.5+/40 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + + Total SPIN, PARITY = 40.5 -, 2 chs, 0 cc. Rmin & Coul turning = 1.3 32.4 + S-matrix 1 = 1.00000 0.00000 for L= 41, J= 40.5 channel on core I = 0.0 from L= 41, Acc. loss = 0.0 D. + Elastic phase shift 1 = 0.000 -0.000 deg. for the L = 41, J = 40.5 channel. + 40.5 1.00000000 0.00000000i: elastic S-matrix @@ 0.00 0 0 F 0 2 2 T + Reaction Xsec 40.5-/41 @ 1 = 0.000#, Out: 0.000# 0.000# -0.000f + + Finished all CC sets @ 1.46559998E-02 +0CUMULATIVE REACTION cross section = 1024.59584 = 4.88 = 27.9 +0CUMULATIVE ELASTIC cross section = 0.00000 +0CUMULATIVE outgoing cross sections in partition 1 : 0.00000 +0CUMULATIVE outgoing cross sections in partition 2 : 7.65525 +0Cumulative ABSORBTION by Imaginary Potentials = 1016.94059 = 4.89 = 28.0 + Fusion for specific p M-states : 1016.940593 1016.940593 +0CUMULATIVE OUTGOING cross section = 7.65525 + Strength functions * 10^4 for L=0-2 = 0.22458 0.23477 2.23763 (with r0= 1.35 fm). R' = -0.742 fm + To convert to S-factors (MeV.mb = keV.b), multiply by 9.7251E+02 + + + CROSS SECTIONS FOR OUTGOING p & 48Ca in state # 1 with spins & parities 0.5 + & 0.0 +; 0 + + 0.01 deg.: X-S = 7.603745E+15 mb/sr, & pols = 0. 0.00000 ++ /R = 9.999987E-01 + 1.00 deg.: X-S = 7.576292E+07 mb/sr, & pols = 0. 0.00001 ++ /R = 9.963377E-01 + 2.00 deg.: X-S = 4.541940E+06 mb/sr, & pols = 0. 0.00021 ++ /R = 9.555317E-01 + 3.00 deg.: X-S = 8.326224E+05 mb/sr, & pols = 0. 0.00086 ++ /R = 8.865569E-01 + 4.00 deg.: X-S = 2.403761E+05 mb/sr, & pols =-0. 0.00210 ++ /R = 8.086311E-01 + 5.00 deg.: X-S = 8.999523E+04 mb/sr, & pols = 0. 0.00378 ++ /R = 7.387885E-01 + 6.00 deg.: X-S = 4.058407E+04 mb/sr, & pols =-0. 0.00536 ++ /R = 6.904602E-01 + 7.00 deg.: X-S = 2.137141E+04 mb/sr, & pols =-0. 0.00584 ++ /R = 6.731576E-01 + 8.00 deg.: X-S = 1.289473E+04 mb/sr, & pols = 0. 0.00419 ++ /R = 6.923610E-01 + 9.00 deg.: X-S = 8722.267110 mb/sr, & pols = 0. 0.00000 ++ /R = 7.495231E-01 + 10.00 deg.: X-S = 6436.615789 mb/sr, & pols = 0. -0.00634 ++ /R = 8.422171E-01 + 11.00 deg.: X-S = 5039.804691 mb/sr, & pols = 0. -0.01413 ++ /R = 9.644676E-01 + 12.00 deg.: X-S = 4090.132923 mb/sr, & pols = 0. -0.02276 ++ /R = 1.107282E+00 + 13.00 deg.: X-S = 3381.725589 mb/sr, & pols = 0. -0.03191 ++ /R = 1.259377E+00 + 14.00 deg.: X-S = 2814.921592 mb/sr, & pols = 0. -0.04152 ++ /R = 1.408078E+00 + 15.00 deg.: X-S = 2340.127930 mb/sr, & pols = 0. -0.05170 ++ /R = 1.540326E+00 + 16.00 deg.: X-S = 1932.112095 mb/sr, & pols = 0. -0.06265 ++ /R = 1.643751E+00 + 17.00 deg.: X-S = 1577.692943 mb/sr, & pols = 0. -0.07467 ++ /R = 1.707707E+00 + 18.00 deg.: X-S = 1269.637200 mb/sr, & pols = 0. -0.08815 ++ /R = 1.724217E+00 + 19.00 deg.: X-S = 1003.557686 mb/sr, & pols = 0. -0.10361 ++ /R = 1.688736E+00 + 20.00 deg.: X-S = 776.318960 mb/sr, & pols = 0. -0.12177 ++ /R = 1.600676E+00 + 21.00 deg.: X-S = 585.224897 mb/sr, & pols =-0. -0.14363 ++ /R = 1.463651E+00 + 22.00 deg.: X-S = 427.623046 mb/sr, & pols =-0. -0.17070 ++ /R = 1.285402E+00 + 23.00 deg.: X-S = 300.735647 mb/sr, & pols =-0. -0.20530 ++ /R = 1.077429E+00 + 24.00 deg.: X-S = 201.615023 mb/sr, & pols =-0. -0.25114 ++ /R = 8.543208E-01 + 25.00 deg.: X-S = 127.166427 mb/sr, & pols =-0. -0.31426 ++ /R = 6.328516E-01 + 26.00 deg.: X-S = 74.205538 mb/sr, & pols =-0. -0.40390 ++ /R = 4.308944E-01 + 27.00 deg.: X-S = 39.531108 mb/sr, & pols =-0. -0.52738 ++ /R = 2.662333E-01 + 28.00 deg.: X-S = 20.000781 mb/sr, & pols =-0. -0.63666 ++ /R = 1.553567E-01 + 29.00 deg.: X-S = 12.602653 mb/sr, & pols =-0. -0.45414 ++ /R = 1.123163E-01 + 30.00 deg.: X-S = 14.517974 mb/sr, & pols = 0. 0.00919 ++ /R = 1.477316E-01 + 31.00 deg.: X-S = 23.172316 mb/sr, & pols = 0. 0.20830 ++ /R = 2.680076E-01 + 32.00 deg.: X-S = 36.273862 mb/sr, & pols = 0. 0.23113 ++ /R = 4.748195E-01 + 33.00 deg.: X-S = 51.838497 mb/sr, & pols = 0. 0.20955 ++ /R = 7.648954E-01 + 34.00 deg.: X-S = 68.202097 mb/sr, & pols = 0. 0.18104 ++ /R = 1.130112E+00 + 35.00 deg.: X-S = 84.020994 mb/sr, & pols = 0. 0.15404 ++ /R = 1.557892E+00 + 36.00 deg.: X-S = 98.261992 mb/sr, & pols = 0. 0.12993 ++ /R = 2.031877E+00 + 37.00 deg.: X-S = 110.183577 mb/sr, & pols = 0. 0.10846 ++ /R = 2.532824E+00 + 38.00 deg.: X-S = 119.310120 mb/sr, & pols = 0. 0.08906 ++ /R = 3.039677E+00 + 39.00 deg.: X-S = 125.400932 mb/sr, & pols = 0. 0.07119 ++ /R = 3.530728E+00 + 40.00 deg.: X-S = 128.415967 mb/sr, & pols = 0. 0.05435 ++ /R = 3.984815E+00 + 41.00 deg.: X-S = 128.479886 mb/sr, & pols = 0. 0.03818 ++ /R = 4.382468E+00 + 42.00 deg.: X-S = 125.846023 mb/sr, & pols = 0. 0.02235 ++ /R = 4.706939E+00 + 43.00 deg.: X-S = 120.861569 mb/sr, & pols = 0. 0.00657 ++ /R = 4.945069E+00 + 44.00 deg.: X-S = 113.935089 mb/sr, & pols = 0. -0.00938 ++ /R = 5.087935E+00 + 45.00 deg.: X-S = 105.507198 mb/sr, & pols = 0. -0.02572 ++ /R = 5.131250E+00 + 46.00 deg.: X-S = 96.025036 mb/sr, & pols = 0. -0.04263 ++ /R = 5.075501E+00 + 47.00 deg.: X-S = 85.920879 mb/sr, & pols = 0. -0.06026 ++ /R = 4.925835E+00 + 48.00 deg.: X-S = 75.595069 mb/sr, & pols = 0. -0.07871 ++ /R = 4.691688E+00 + 49.00 deg.: X-S = 65.403215 mb/sr, & pols = 0. -0.09799 ++ /R = 4.386209E+00 + 50.00 deg.: X-S = 55.647468 mb/sr, & pols =-0. -0.11798 ++ /R = 4.025510E+00 + 51.00 deg.: X-S = 46.571559 mb/sr, & pols =-0. -0.13827 ++ /R = 3.627777E+00 + 52.00 deg.: X-S = 38.359162 mb/sr, & pols =-0. -0.15803 ++ /R = 3.212324E+00 + 53.00 deg.: X-S = 31.135112 mb/sr, & pols =-0. -0.17563 ++ /R = 2.798612E+00 + 54.00 deg.: X-S = 24.968947 mb/sr, & pols =-0. -0.18818 ++ /R = 2.405303E+00 + 55.00 deg.: X-S = 19.880244 mb/sr, & pols =-0. -0.19093 ++ /R = 2.049399E+00 + 56.00 deg.: X-S = 15.845234 mb/sr, & pols =-0. -0.17686 ++ /R = 1.745494E+00 + 57.00 deg.: X-S = 12.804218 mb/sr, & pols =-0. -0.13754 ++ /R = 1.505175E+00 + 58.00 deg.: X-S = 10.669328 mb/sr, & pols = 0. -0.06665 ++ /R = 1.336609E+00 + 59.00 deg.: X-S = 9.332273 mb/sr, & pols = 0. 0.03341 ++ /R = 1.244300E+00 + 60.00 deg.: X-S = 8.671728 mb/sr, & pols = 0. 0.14717 ++ /R = 1.229046E+00 + 61.00 deg.: X-S = 8.560139 mb/sr, & pols = 0. 0.25189 ++ /R = 1.288068E+00 + 62.00 deg.: X-S = 8.869732 mb/sr, & pols = 0. 0.33046 ++ /R = 1.415306E+00 + 63.00 deg.: X-S = 9.477622 mb/sr, & pols = 0. 0.37838 ++ /R = 1.601851E+00 + 64.00 deg.: X-S = 10.269932 mb/sr, & pols = 0. 0.40039 ++ /R = 1.836491E+00 + 65.00 deg.: X-S = 11.144932 mb/sr, & pols = 0. 0.40419 ++ /R = 2.106328E+00 + 66.00 deg.: X-S = 12.015205 mb/sr, & pols = 0. 0.39659 ++ /R = 2.397445E+00 + 67.00 deg.: X-S = 12.808900 mb/sr, & pols = 0. 0.38241 ++ /R = 2.695580E+00 + 68.00 deg.: X-S = 13.470185 mb/sr, & pols = 0. 0.36475 ++ /R = 2.986776E+00 + 69.00 deg.: X-S = 13.958976 mb/sr, & pols = 0. 0.34540 ++ /R = 3.257978E+00 + 70.00 deg.: X-S = 14.250089 mb/sr, & pols = 0. 0.32541 ++ /R = 3.497560E+00 + 71.00 deg.: X-S = 14.331921 mb/sr, & pols = 0. 0.30531 ++ /R = 3.695753E+00 + 72.00 deg.: X-S = 14.204794 mb/sr, & pols = 0. 0.28535 ++ /R = 3.844962E+00 + 73.00 deg.: X-S = 13.879078 mb/sr, & pols = 0. 0.26563 ++ /R = 3.939975E+00 + 74.00 deg.: X-S = 13.373201 mb/sr, & pols = 0. 0.24614 ++ /R = 3.978047E+00 + 75.00 deg.: X-S = 12.711636 mb/sr, & pols = 0. 0.22684 ++ /R = 3.958881E+00 + 76.00 deg.: X-S = 11.922962 mb/sr, & pols = 0. 0.20768 ++ /R = 3.884495E+00 + 77.00 deg.: X-S = 11.038050 mb/sr, & pols = 0. 0.18861 ++ /R = 3.759006E+00 + 78.00 deg.: X-S = 10.088433 mb/sr, & pols = 0. 0.16962 ++ /R = 3.588338E+00 + 79.00 deg.: X-S = 9.104896 mb/sr, & pols = 0. 0.15075 ++ /R = 3.379866E+00 + 80.00 deg.: X-S = 8.116309 mb/sr, & pols = 0. 0.13214 ++ /R = 3.142035E+00 + 81.00 deg.: X-S = 7.148704 mb/sr, & pols =-0. 0.11408 ++ /R = 2.883948E+00 + 82.00 deg.: X-S = 6.224603 mb/sr, & pols =-0. 0.09708 ++ /R = 2.614964E+00 + 83.00 deg.: X-S = 5.362590 mb/sr, & pols =-0. 0.08199 ++ /R = 2.344311E+00 + 84.00 deg.: X-S = 4.577087 mb/sr, & pols =-0. 0.07010 ++ /R = 2.080727E+00 + 85.00 deg.: X-S = 3.878337 mb/sr, & pols =-0. 0.06336 ++ /R = 1.832151E+00 + 86.00 deg.: X-S = 3.272548 mb/sr, & pols =-0. 0.06450 ++ /R = 1.605469E+00 + 87.00 deg.: X-S = 2.762172 mb/sr, & pols =-0. 0.07699 ++ /R = 1.406313E+00 + 88.00 deg.: X-S = 2.346289 mb/sr, & pols =-0. 0.10469 ++ /R = 1.238936E+00 + 89.00 deg.: X-S = 2.021067 mb/sr, & pols = 0. 0.15075 ++ /R = 1.106141E+00 + 90.00 deg.: X-S = 1.780273 mb/sr, & pols = 0. 0.21587 ++ /R = 1.009274E+00 + 91.00 deg.: X-S = 1.615795 mb/sr, & pols = 0. 0.29643 ++ /R = 9.482807E-01 + 92.00 deg.: X-S = 1.518168 mb/sr, & pols = 0. 0.38418 ++ /R = 9.218043E-01 + 93.00 deg.: X-S = 1.477081 mb/sr, & pols = 0. 0.46855 ++ /R = 9.273324E-01 + 94.00 deg.: X-S = 1.481836 mb/sr, & pols = 0. 0.54041 ++ /R = 9.613740E-01 + 95.00 deg.: X-S = 1.521772 mb/sr, & pols = 0. 0.59485 ++ /R = 1.019660E+00 + 96.00 deg.: X-S = 1.586620 mb/sr, & pols = 0. 0.63142 ++ /R = 1.097360E+00 + 97.00 deg.: X-S = 1.666799 mb/sr, & pols = 0. 0.65253 ++ /R = 1.189297E+00 + 98.00 deg.: X-S = 1.753652 mb/sr, & pols = 0. 0.66169 ++ /R = 1.290165E+00 + 99.00 deg.: X-S = 1.839608 mb/sr, & pols = 0. 0.66229 ++ /R = 1.394728E+00 + 100.00 deg.: X-S = 1.918293 mb/sr, & pols = 0. 0.65711 ++ /R = 1.498005E+00 + 101.00 deg.: X-S = 1.984583 mb/sr, & pols = 0. 0.64821 ++ /R = 1.595423E+00 + 102.00 deg.: X-S = 2.034600 mb/sr, & pols = 0. 0.63703 ++ /R = 1.682953E+00 + 103.00 deg.: X-S = 2.065673 mb/sr, & pols = 0. 0.62451 ++ /R = 1.757202E+00 + 104.00 deg.: X-S = 2.076262 mb/sr, & pols = 0. 0.61126 ++ /R = 1.815487E+00 + 105.00 deg.: X-S = 2.065848 mb/sr, & pols = 0. 0.59766 ++ /R = 1.855870E+00 + 106.00 deg.: X-S = 2.034816 mb/sr, & pols = 0. 0.58391 ++ /R = 1.877163E+00 + 107.00 deg.: X-S = 1.984308 mb/sr, & pols = 0. 0.57010 ++ /R = 1.878912E+00 + 108.00 deg.: X-S = 1.916087 mb/sr, & pols = 0. 0.55623 ++ /R = 1.861351E+00 + 109.00 deg.: X-S = 1.832388 mb/sr, & pols = 0. 0.54228 ++ /R = 1.825341E+00 + 110.00 deg.: X-S = 1.735778 mb/sr, & pols = 0. 0.52817 ++ /R = 1.772290E+00 + 111.00 deg.: X-S = 1.629031 mb/sr, & pols = 0. 0.51381 ++ /R = 1.704067E+00 + 112.00 deg.: X-S = 1.514999 mb/sr, & pols = 0. 0.49907 ++ /R = 1.622900E+00 + 113.00 deg.: X-S = 1.396518 mb/sr, & pols = 0. 0.48386 ++ /R = 1.531282E+00 + 114.00 deg.: X-S = 1.276310 mb/sr, & pols = 0. 0.46806 ++ /R = 1.431872E+00 + 115.00 deg.: X-S = 1.156915 mb/sr, & pols = 0. 0.45159 ++ /R = 1.327396E+00 + 116.00 deg.: X-S = 1.040634 mb/sr, & pols = 0. 0.43441 ++ /R = 1.220569E+00 + 117.00 deg.: X-S = 0.929486 mb/sr, & pols = 0. 0.41656 ++ /R = 1.114008E+00 + 118.00 deg.: X-S = 0.825182 mb/sr, & pols = 0. 0.39818 ++ /R = 1.010170E+00 + 119.00 deg.: X-S = 0.729113 mb/sr, & pols = 0. 0.37962 ++ /R = 9.112942E-01 + 120.00 deg.: X-S = 0.642344 mb/sr, & pols = 0. 0.36150 ++ /R = 8.193562E-01 + 121.00 deg.: X-S = 0.565628 mb/sr, & pols = 0. 0.34475 ++ /R = 7.360377E-01 + 122.00 deg.: X-S = 0.499413 mb/sr, & pols = 0. 0.33075 ++ /R = 6.627038E-01 + 123.00 deg.: X-S = 0.443872 mb/sr, & pols = 0. 0.32125 ++ /R = 6.003914E-01 + 124.00 deg.: X-S = 0.398923 mb/sr, & pols = 0. 0.31821 ++ /R = 5.498077E-01 + 125.00 deg.: X-S = 0.364257 mb/sr, & pols = 0. 0.32340 ++ /R = 5.113358E-01 + 126.00 deg.: X-S = 0.339374 mb/sr, & pols = 0. 0.33779 ++ /R = 4.850476E-01 + 127.00 deg.: X-S = 0.323606 mb/sr, & pols = 0. 0.36104 ++ /R = 4.707215E-01 + 128.00 deg.: X-S = 0.316154 mb/sr, & pols = 0. 0.39129 ++ /R = 4.678658E-01 + 129.00 deg.: X-S = 0.316109 mb/sr, & pols = 0. 0.42554 ++ /R = 4.757436E-01 + 130.00 deg.: X-S = 0.322489 mb/sr, & pols = 0. 0.46047 ++ /R = 4.934014E-01 + 131.00 deg.: X-S = 0.334255 mb/sr, & pols = 0. 0.49327 ++ /R = 5.196990E-01 + 132.00 deg.: X-S = 0.350340 mb/sr, & pols = 0. 0.52203 ++ /R = 5.533405E-01 + 133.00 deg.: X-S = 0.369669 mb/sr, & pols = 0. 0.54584 ++ /R = 5.929063E-01 + 134.00 deg.: X-S = 0.391178 mb/sr, & pols = 0. 0.56457 ++ /R = 6.368852E-01 + 135.00 deg.: X-S = 0.413834 mb/sr, & pols = 0. 0.57856 ++ /R = 6.837076E-01 + 136.00 deg.: X-S = 0.436650 mb/sr, & pols = 0. 0.58840 ++ /R = 7.317778E-01 + 137.00 deg.: X-S = 0.458698 mb/sr, & pols = 0. 0.59476 ++ /R = 7.795082E-01 + 138.00 deg.: X-S = 0.479126 mb/sr, & pols = 0. 0.59826 ++ /R = 8.253515E-01 + 139.00 deg.: X-S = 0.497169 mb/sr, & pols = 0. 0.59949 ++ /R = 8.678341E-01 + 140.00 deg.: X-S = 0.512160 mb/sr, & pols = 0. 0.59892 ++ /R = 9.055887E-01 + 141.00 deg.: X-S = 0.523540 mb/sr, & pols = 0. 0.59697 ++ /R = 9.373854E-01 + 142.00 deg.: X-S = 0.530867 mb/sr, & pols = 0. 0.59398 ++ /R = 9.621618E-01 + 143.00 deg.: X-S = 0.533822 mb/sr, & pols = 0. 0.59022 ++ /R = 9.790504E-01 + 144.00 deg.: X-S = 0.532215 mb/sr, & pols = 0. 0.58592 ++ /R = 9.874034E-01 + 145.00 deg.: X-S = 0.525987 mb/sr, & pols = 0. 0.58127 ++ /R = 9.868136E-01 + 146.00 deg.: X-S = 0.515210 mb/sr, & pols = 0. 0.57639 ++ /R = 9.771295E-01 + 147.00 deg.: X-S = 0.500088 mb/sr, & pols = 0. 0.57140 ++ /R = 9.584664E-01 + 148.00 deg.: X-S = 0.480948 mb/sr, & pols = 0. 0.56636 ++ /R = 9.312089E-01 + 149.00 deg.: X-S = 0.458234 mb/sr, & pols = 0. 0.56128 ++ /R = 8.960083E-01 + 150.00 deg.: X-S = 0.432498 mb/sr, & pols = 0. 0.55612 ++ /R = 8.537706E-01 + 151.00 deg.: X-S = 0.404381 mb/sr, & pols = 0. 0.55076 ++ /R = 8.056373E-01 + 152.00 deg.: X-S = 0.374604 mb/sr, & pols = 0. 0.54496 ++ /R = 7.529580E-01 + 153.00 deg.: X-S = 0.343941 mb/sr, & pols = 0. 0.53835 ++ /R = 6.972560E-01 + 154.00 deg.: X-S = 0.313204 mb/sr, & pols = 0. 0.53034 ++ /R = 6.401853E-01 + 155.00 deg.: X-S = 0.283218 mb/sr, & pols = 0. 0.52007 ++ /R = 5.834838E-01 + 156.00 deg.: X-S = 0.254794 mb/sr, & pols = 0. 0.50633 ++ /R = 5.289198E-01 + 157.00 deg.: X-S = 0.228712 mb/sr, & pols = 0. 0.48755 ++ /R = 4.782362E-01 + 158.00 deg.: X-S = 0.205690 mb/sr, & pols = 0. 0.46185 ++ /R = 4.330938E-01 + 159.00 deg.: X-S = 0.186366 mb/sr, & pols = 0. 0.42739 ++ /R = 3.950141E-01 + 160.00 deg.: X-S = 0.171274 mb/sr, & pols = 0. 0.38297 ++ /R = 3.653257E-01 + 161.00 deg.: X-S = 0.160831 mb/sr, & pols = 0. 0.32897 ++ /R = 3.451147E-01 + 162.00 deg.: X-S = 0.155317 mb/sr, & pols = 0. 0.26812 ++ /R = 3.351827E-01 + 163.00 deg.: X-S = 0.154867 mb/sr, & pols = 0. 0.20537 ++ /R = 3.360125E-01 + 164.00 deg.: X-S = 0.159465 mb/sr, & pols = 0. 0.14650 ++ /R = 3.477442E-01 + 165.00 deg.: X-S = 0.168941 mb/sr, & pols = 0. 0.09624 ++ /R = 3.701628E-01 + 166.00 deg.: X-S = 0.182976 mb/sr, & pols = 0. 0.05693 ++ /R = 4.026981E-01 + 167.00 deg.: X-S = 0.201108 mb/sr, & pols = 0. 0.02850 ++ /R = 4.444363E-01 + 168.00 deg.: X-S = 0.222748 mb/sr, & pols = 0. 0.00939 ++ /R = 4.941452E-01 + 169.00 deg.: X-S = 0.247196 mb/sr, & pols = 0. -0.00253 ++ /R = 5.503105E-01 + 170.00 deg.: X-S = 0.273661 mb/sr, & pols = 0. -0.00929 ++ /R = 6.111835E-01 + 171.00 deg.: X-S = 0.301287 mb/sr, & pols = 0. -0.01255 ++ /R = 6.748369E-01 + 172.00 deg.: X-S = 0.329180 mb/sr, & pols = 0. -0.01354 ++ /R = 7.392295E-01 + 173.00 deg.: X-S = 0.356438 mb/sr, & pols = 0. -0.01313 ++ /R = 8.022751E-01 + 174.00 deg.: X-S = 0.382176 mb/sr, & pols = 0. -0.01188 ++ /R = 8.619135E-01 + 175.00 deg.: X-S = 0.405559 mb/sr, & pols = 0. -0.01018 ++ /R = 9.161828E-01 + 176.00 deg.: X-S = 0.425826 mb/sr, & pols = 0. -0.00825 ++ /R = 9.632877E-01 + 177.00 deg.: X-S = 0.442318 mb/sr, & pols = 0. -0.00621 ++ /R = 1.001663E+00 + 178.00 deg.: X-S = 0.454498 mb/sr, & pols = 0. -0.00414 ++ /R = 1.030029E+00 + 179.00 deg.: X-S = 0.461968 mb/sr, & pols = 0. -0.00207 ++ /R = 1.047438E+00 + 179.99 deg.: X-S = 0.464485 mb/sr, & pols = 0. -0.00002 ++ /R = 1.053305E+00 + Integrated 1.4553E+11 mb, over [ 0.000, 180.000] at 35.0000 MeV + + CROSS SECTIONS FOR OUTGOING n & 48Sc in state # 1 with spins & parities 0.5 + & 0.0 +; 0 + + 0.00 deg.: X-S = 1.667299 mb/sr, & pols =-0. 0.00000 ++ REAC 7.6553E+00 mb + 1.00 deg.: X-S = 1.673901 mb/sr, & pols =-0. -0.01913 + 2.00 deg.: X-S = 1.693668 mb/sr, & pols =-0. -0.03763 + 3.00 deg.: X-S = 1.726482 mb/sr, & pols =-0. -0.05492 + 4.00 deg.: X-S = 1.772133 mb/sr, & pols =-0. -0.07053 + 5.00 deg.: X-S = 1.830297 mb/sr, & pols =-0. -0.08415 + 6.00 deg.: X-S = 1.900509 mb/sr, & pols =-0. -0.09559 + 7.00 deg.: X-S = 1.982140 mb/sr, & pols =-0. -0.10482 + 8.00 deg.: X-S = 2.074362 mb/sr, & pols =-0. -0.11193 + 9.00 deg.: X-S = 2.176130 mb/sr, & pols =-0. -0.11711 + 10.00 deg.: X-S = 2.286157 mb/sr, & pols =-0. -0.12061 + 11.00 deg.: X-S = 2.402911 mb/sr, & pols =-0. -0.12270 + 12.00 deg.: X-S = 2.524612 mb/sr, & pols =-0. -0.12366 + 13.00 deg.: X-S = 2.649250 mb/sr, & pols =-0. -0.12378 + 14.00 deg.: X-S = 2.774614 mb/sr, & pols =-0. -0.12331 + 15.00 deg.: X-S = 2.898329 mb/sr, & pols =-0. -0.12245 + 16.00 deg.: X-S = 3.017913 mb/sr, & pols =-0. -0.12140 + 17.00 deg.: X-S = 3.130842 mb/sr, & pols =-0. -0.12030 + 18.00 deg.: X-S = 3.234616 mb/sr, & pols =-0. -0.11929 + 19.00 deg.: X-S = 3.326838 mb/sr, & pols =-0. -0.11846 + 20.00 deg.: X-S = 3.405291 mb/sr, & pols =-0. -0.11788 + 21.00 deg.: X-S = 3.468009 mb/sr, & pols =-0. -0.11760 + 22.00 deg.: X-S = 3.513343 mb/sr, & pols =-0. -0.11765 + 23.00 deg.: X-S = 3.540023 mb/sr, & pols = 0. -0.11807 + 24.00 deg.: X-S = 3.547196 mb/sr, & pols = 0. -0.11886 + 25.00 deg.: X-S = 3.534464 mb/sr, & pols = 0. -0.12002 + 26.00 deg.: X-S = 3.501889 mb/sr, & pols = 0. -0.12154 + 27.00 deg.: X-S = 3.449999 mb/sr, & pols = 0. -0.12342 + 28.00 deg.: X-S = 3.379764 mb/sr, & pols = 0. -0.12562 + 29.00 deg.: X-S = 3.292561 mb/sr, & pols = 0. -0.12812 + 30.00 deg.: X-S = 3.190130 mb/sr, & pols = 0. -0.13087 + 31.00 deg.: X-S = 3.074510 mb/sr, & pols = 0. -0.13383 + 32.00 deg.: X-S = 2.947972 mb/sr, & pols = 0. -0.13693 + 33.00 deg.: X-S = 2.812943 mb/sr, & pols = 0. -0.14011 + 34.00 deg.: X-S = 2.671931 mb/sr, & pols = 0. -0.14328 + 35.00 deg.: X-S = 2.527445 mb/sr, & pols = 0. -0.14632 + 36.00 deg.: X-S = 2.381925 mb/sr, & pols = 0. -0.14913 + 37.00 deg.: X-S = 2.237669 mb/sr, & pols = 0. -0.15156 + 38.00 deg.: X-S = 2.096781 mb/sr, & pols = 0. -0.15345 + 39.00 deg.: X-S = 1.961116 mb/sr, & pols = 0. -0.15465 + 40.00 deg.: X-S = 1.832242 mb/sr, & pols = 0. -0.15499 + 41.00 deg.: X-S = 1.711421 mb/sr, & pols = 0. -0.15429 + 42.00 deg.: X-S = 1.599585 mb/sr, & pols = 0. -0.15243 + 43.00 deg.: X-S = 1.497344 mb/sr, & pols = 0. -0.14929 + 44.00 deg.: X-S = 1.404990 mb/sr, & pols = 0. -0.14483 + 45.00 deg.: X-S = 1.322518 mb/sr, & pols = 0. -0.13906 + 46.00 deg.: X-S = 1.249652 mb/sr, & pols = 0. -0.13211 + 47.00 deg.: X-S = 1.185884 mb/sr, & pols = 0. -0.12414 + 48.00 deg.: X-S = 1.130508 mb/sr, & pols = 0. -0.11542 + 49.00 deg.: X-S = 1.082667 mb/sr, & pols = 0. -0.10626 + 50.00 deg.: X-S = 1.041394 mb/sr, & pols = 0. -0.09699 + 51.00 deg.: X-S = 1.005659 mb/sr, & pols = 0. -0.08794 + 52.00 deg.: X-S = 0.974405 mb/sr, & pols = 0. -0.07940 + 53.00 deg.: X-S = 0.946594 mb/sr, & pols = 0. -0.07160 + 54.00 deg.: X-S = 0.921232 mb/sr, & pols = 0. -0.06472 + 55.00 deg.: X-S = 0.897408 mb/sr, & pols = 0. -0.05885 + 56.00 deg.: X-S = 0.874307 mb/sr, & pols = 0. -0.05403 + 57.00 deg.: X-S = 0.851234 mb/sr, & pols = 0. -0.05022 + 58.00 deg.: X-S = 0.827624 mb/sr, & pols = 0. -0.04736 + 59.00 deg.: X-S = 0.803046 mb/sr, & pols = 0. -0.04533 + 60.00 deg.: X-S = 0.777204 mb/sr, & pols = 0. -0.04400 + 61.00 deg.: X-S = 0.749933 mb/sr, & pols = 0. -0.04321 + 62.00 deg.: X-S = 0.721188 mb/sr, & pols = 0. -0.04280 + 63.00 deg.: X-S = 0.691036 mb/sr, & pols = 0. -0.04259 + 64.00 deg.: X-S = 0.659635 mb/sr, & pols = 0. -0.04238 + 65.00 deg.: X-S = 0.627223 mb/sr, & pols = 0. -0.04199 + 66.00 deg.: X-S = 0.594097 mb/sr, & pols = 0. -0.04119 + 67.00 deg.: X-S = 0.560596 mb/sr, & pols = 0. -0.03978 + 68.00 deg.: X-S = 0.527085 mb/sr, & pols = 0. -0.03753 + 69.00 deg.: X-S = 0.493933 mb/sr, & pols = 0. -0.03422 + 70.00 deg.: X-S = 0.461504 mb/sr, & pols = 0. -0.02962 + 71.00 deg.: X-S = 0.430140 mb/sr, & pols = 0. -0.02351 + 72.00 deg.: X-S = 0.400149 mb/sr, & pols = 0. -0.01568 + 73.00 deg.: X-S = 0.371798 mb/sr, & pols = 0. -0.00599 + 74.00 deg.: X-S = 0.345306 mb/sr, & pols = 0. 0.00568 + 75.00 deg.: X-S = 0.320838 mb/sr, & pols = 0. 0.01934 + 76.00 deg.: X-S = 0.298502 mb/sr, & pols = 0. 0.03492 + 77.00 deg.: X-S = 0.278355 mb/sr, & pols = 0. 0.05220 + 78.00 deg.: X-S = 0.260395 mb/sr, & pols = 0. 0.07084 + 79.00 deg.: X-S = 0.244575 mb/sr, & pols = 0. 0.09034 + 80.00 deg.: X-S = 0.230801 mb/sr, & pols = 0. 0.11013 + 81.00 deg.: X-S = 0.218940 mb/sr, & pols = 0. 0.12954 + 82.00 deg.: X-S = 0.208828 mb/sr, & pols = 0. 0.14792 + 83.00 deg.: X-S = 0.200275 mb/sr, & pols = 0. 0.16468 + 84.00 deg.: X-S = 0.193075 mb/sr, & pols = 0. 0.17933 + 85.00 deg.: X-S = 0.187013 mb/sr, & pols = 0. 0.19159 + 86.00 deg.: X-S = 0.181871 mb/sr, & pols = 0. 0.20133 + 87.00 deg.: X-S = 0.177436 mb/sr, & pols = 0. 0.20860 + 88.00 deg.: X-S = 0.173505 mb/sr, & pols = 0. 0.21362 + 89.00 deg.: X-S = 0.169892 mb/sr, & pols = 0. 0.21670 + 90.00 deg.: X-S = 0.166428 mb/sr, & pols = 0. 0.21826 + 91.00 deg.: X-S = 0.162970 mb/sr, & pols = 0. 0.21873 + 92.00 deg.: X-S = 0.159400 mb/sr, & pols = 0. 0.21856 + 93.00 deg.: X-S = 0.155626 mb/sr, & pols = 0. 0.21821 + 94.00 deg.: X-S = 0.151584 mb/sr, & pols = 0. 0.21808 + 95.00 deg.: X-S = 0.147234 mb/sr, & pols = 0. 0.21857 + 96.00 deg.: X-S = 0.142563 mb/sr, & pols = 0. 0.22003 + 97.00 deg.: X-S = 0.137579 mb/sr, & pols = 0. 0.22277 + 98.00 deg.: X-S = 0.132312 mb/sr, & pols = 0. 0.22709 + 99.00 deg.: X-S = 0.126807 mb/sr, & pols = 0. 0.23320 + 100.00 deg.: X-S = 0.121121 mb/sr, & pols = 0. 0.24131 + 101.00 deg.: X-S = 0.115322 mb/sr, & pols = 0. 0.25157 + 102.00 deg.: X-S = 0.109484 mb/sr, & pols = 0. 0.26404 + 103.00 deg.: X-S = 0.103683 mb/sr, & pols = 0. 0.27875 + 104.00 deg.: X-S = 0.097992 mb/sr, & pols = 0. 0.29560 + 105.00 deg.: X-S = 0.092482 mb/sr, & pols = 0. 0.31441 + 106.00 deg.: X-S = 0.087218 mb/sr, & pols = 0. 0.33486 + 107.00 deg.: X-S = 0.082253 mb/sr, & pols = 0. 0.35650 + 108.00 deg.: X-S = 0.077633 mb/sr, & pols = 0. 0.37874 + 109.00 deg.: X-S = 0.073391 mb/sr, & pols = 0. 0.40090 + 110.00 deg.: X-S = 0.069548 mb/sr, & pols = 0. 0.42217 + 111.00 deg.: X-S = 0.066115 mb/sr, & pols = 0. 0.44175 + 112.00 deg.: X-S = 0.063089 mb/sr, & pols = 0. 0.45886 + 113.00 deg.: X-S = 0.060459 mb/sr, & pols = 0. 0.47284 + 114.00 deg.: X-S = 0.058204 mb/sr, & pols = 0. 0.48323 + 115.00 deg.: X-S = 0.056295 mb/sr, & pols = 0. 0.48984 + 116.00 deg.: X-S = 0.054698 mb/sr, & pols = 0. 0.49272 + 117.00 deg.: X-S = 0.053375 mb/sr, & pols = 0. 0.49224 + 118.00 deg.: X-S = 0.052284 mb/sr, & pols = 0. 0.48898 + 119.00 deg.: X-S = 0.051384 mb/sr, & pols = 0. 0.48372 + 120.00 deg.: X-S = 0.050635 mb/sr, & pols = 0. 0.47731 + 121.00 deg.: X-S = 0.050000 mb/sr, & pols = 0. 0.47066 + 122.00 deg.: X-S = 0.049443 mb/sr, & pols = 0. 0.46464 + 123.00 deg.: X-S = 0.048937 mb/sr, & pols = 0. 0.46003 + 124.00 deg.: X-S = 0.048455 mb/sr, & pols = 0. 0.45752 + 125.00 deg.: X-S = 0.047977 mb/sr, & pols = 0. 0.45762 + 126.00 deg.: X-S = 0.047490 mb/sr, & pols = 0. 0.46073 + 127.00 deg.: X-S = 0.046984 mb/sr, & pols = 0. 0.46707 + 128.00 deg.: X-S = 0.046452 mb/sr, & pols = 0. 0.47669 + 129.00 deg.: X-S = 0.045892 mb/sr, & pols = 0. 0.48951 + 130.00 deg.: X-S = 0.045306 mb/sr, & pols = 0. 0.50529 + 131.00 deg.: X-S = 0.044695 mb/sr, & pols = 0. 0.52363 + 132.00 deg.: X-S = 0.044061 mb/sr, & pols = 0. 0.54401 + 133.00 deg.: X-S = 0.043409 mb/sr, & pols = 0. 0.56582 + 134.00 deg.: X-S = 0.042739 mb/sr, & pols = 0. 0.58835 + 135.00 deg.: X-S = 0.042053 mb/sr, & pols = 0. 0.61083 + 136.00 deg.: X-S = 0.041351 mb/sr, & pols = 0. 0.63249 + 137.00 deg.: X-S = 0.040629 mb/sr, & pols = 0. 0.65256 + 138.00 deg.: X-S = 0.039884 mb/sr, & pols = 0. 0.67033 + 139.00 deg.: X-S = 0.039109 mb/sr, & pols = 0. 0.68516 + 140.00 deg.: X-S = 0.038300 mb/sr, & pols = 0. 0.69649 + 141.00 deg.: X-S = 0.037449 mb/sr, & pols = 0. 0.70387 + 142.00 deg.: X-S = 0.036552 mb/sr, & pols = 0. 0.70696 + 143.00 deg.: X-S = 0.035604 mb/sr, & pols = 0. 0.70549 + 144.00 deg.: X-S = 0.034605 mb/sr, & pols = 0. 0.69928 + 145.00 deg.: X-S = 0.033558 mb/sr, & pols = 0. 0.68818 + 146.00 deg.: X-S = 0.032470 mb/sr, & pols = 0. 0.67211 + 147.00 deg.: X-S = 0.031353 mb/sr, & pols = 0. 0.65100 + 148.00 deg.: X-S = 0.030226 mb/sr, & pols = 0. 0.62480 + 149.00 deg.: X-S = 0.029113 mb/sr, & pols = 0. 0.59349 + 150.00 deg.: X-S = 0.028044 mb/sr, & pols = 0. 0.55715 + 151.00 deg.: X-S = 0.027054 mb/sr, & pols = 0. 0.51599 + 152.00 deg.: X-S = 0.026184 mb/sr, & pols = 0. 0.47044 + 153.00 deg.: X-S = 0.025478 mb/sr, & pols = 0. 0.42129 + 154.00 deg.: X-S = 0.024981 mb/sr, & pols = 0. 0.36972 + 155.00 deg.: X-S = 0.024741 mb/sr, & pols = 0. 0.31739 + 156.00 deg.: X-S = 0.024805 mb/sr, & pols = 0. 0.26629 + 157.00 deg.: X-S = 0.025216 mb/sr, & pols = 0. 0.21855 + 158.00 deg.: X-S = 0.026013 mb/sr, & pols = 0. 0.17606 + 159.00 deg.: X-S = 0.027228 mb/sr, & pols = 0. 0.14018 + 160.00 deg.: X-S = 0.028884 mb/sr, & pols = 0. 0.11149 + 161.00 deg.: X-S = 0.030997 mb/sr, & pols = 0. 0.08980 + 162.00 deg.: X-S = 0.033567 mb/sr, & pols = 0. 0.07428 + 163.00 deg.: X-S = 0.036586 mb/sr, & pols = 0. 0.06377 + 164.00 deg.: X-S = 0.040030 mb/sr, & pols = 0. 0.05699 + 165.00 deg.: X-S = 0.043862 mb/sr, & pols = 0. 0.05278 + 166.00 deg.: X-S = 0.048034 mb/sr, & pols = 0. 0.05014 + 167.00 deg.: X-S = 0.052483 mb/sr, & pols = 0. 0.04831 + 168.00 deg.: X-S = 0.057137 mb/sr, & pols = 0. 0.04675 + 169.00 deg.: X-S = 0.061913 mb/sr, & pols = 0. 0.04509 + 170.00 deg.: X-S = 0.066721 mb/sr, & pols = 0. 0.04309 + 171.00 deg.: X-S = 0.071466 mb/sr, & pols = 0. 0.04065 + 172.00 deg.: X-S = 0.076050 mb/sr, & pols = 0. 0.03770 + 173.00 deg.: X-S = 0.080376 mb/sr, & pols = 0. 0.03423 + 174.00 deg.: X-S = 0.084349 mb/sr, & pols = 0. 0.03029 + 175.00 deg.: X-S = 0.087879 mb/sr, & pols = 0. 0.02592 + 176.00 deg.: X-S = 0.090887 mb/sr, & pols = 0. 0.02119 + 177.00 deg.: X-S = 0.093304 mb/sr, & pols = 0. 0.01615 + 178.00 deg.: X-S = 0.095071 mb/sr, & pols = 0. 0.01090 + 179.00 deg.: X-S = 0.096149 mb/sr, & pols = 0. 0.00549 + 180.00 deg.: X-S = 0.096511 mb/sr, & pols = 0. 0.00000 + Integrated 7.6550E+00 mb, over [ 0.000, 180.000] at 35.0000 MeV + Finished all xsecs @ 1.78050008E-02 + + The following files have been created: + 3:local copy of User input. 6:standard output. + 7:elastic S-matrix elements. 13:total cross sections/state. + 16:tables of cross sections. 35:Astrophysics S-factors / Ecm. + 38:cross sections for each J/pi. 39:cross sections for each Ecm. + 40:all cross sectns. for each Elab. 45:scat phase shift as E functions. + 56:Fusion for each Jtotal. 75:S-factors for lab energies. + 201:Separate cross sections. 202:Separate cross sections. + + PARAMETERS : MAXQRN MLOC LMAX1 MCLIST MFNL MPWCOUP + ALLOWED : 1 38 60 36 4 0 + REQUIRED: 0 9 42 4 0 0 + + + ACCURACY ANALYSIS at 35.000 MeV : + + Elastic h*k = 0.025 so OK compared with 0.200 + + Real(S-el) > 0.01 first at J = 0.5 + Real(S-el) > 0.10 first at J = 0.5 + Real(S-el) > 0.50 first at J = 6.5 + Real(S-el) > 0.90 first at J = 7.5 + Real(S-el) > 0.99 first at J = 9.5 + R-turn = 32.37 fm at J = 40.5 + + Forward-angle excitation cut off below 1.488 deg by max JT = 40.5 + and below 2.406 deg by max R. + + Total CPU 0 time = 0.02 seconds diff --git a/tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.csv b/tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.csv new file mode 100644 index 00000000..7383009c --- /dev/null +++ b/tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.csv @@ -0,0 +1,185 @@ +# case_id: F10_p_ca48_pn_ias_35MeV +# reference_code: frescox +# source_example: Ca48_pn_IAS_35MeV.in +# observable: quasielastic_pn +theta_cm_deg,dsdo_mb_per_sr +1.000000,1.673901000000e+00 +2.000000,1.693668000000e+00 +3.000000,1.726482000000e+00 +4.000000,1.772133000000e+00 +5.000000,1.830297000000e+00 +6.000000,1.900509000000e+00 +7.000000,1.982140000000e+00 +8.000000,2.074362000000e+00 +9.000000,2.176130000000e+00 +10.000000,2.286157000000e+00 +11.000000,2.402911000000e+00 +12.000000,2.524612000000e+00 +13.000000,2.649250000000e+00 +14.000000,2.774614000000e+00 +15.000000,2.898329000000e+00 +16.000000,3.017913000000e+00 +17.000000,3.130842000000e+00 +18.000000,3.234616000000e+00 +19.000000,3.326838000000e+00 +20.000000,3.405291000000e+00 +21.000000,3.468009000000e+00 +22.000000,3.513343000000e+00 +23.000000,3.540023000000e+00 +24.000000,3.547196000000e+00 +25.000000,3.534464000000e+00 +26.000000,3.501889000000e+00 +27.000000,3.449999000000e+00 +28.000000,3.379764000000e+00 +29.000000,3.292561000000e+00 +30.000000,3.190130000000e+00 +31.000000,3.074510000000e+00 +32.000000,2.947972000000e+00 +33.000000,2.812943000000e+00 +34.000000,2.671931000000e+00 +35.000000,2.527445000000e+00 +36.000000,2.381925000000e+00 +37.000000,2.237669000000e+00 +38.000000,2.096781000000e+00 +39.000000,1.961116000000e+00 +40.000000,1.832242000000e+00 +41.000000,1.711421000000e+00 +42.000000,1.599585000000e+00 +43.000000,1.497344000000e+00 +44.000000,1.404990000000e+00 +45.000000,1.322518000000e+00 +46.000000,1.249652000000e+00 +47.000000,1.185884000000e+00 +48.000000,1.130508000000e+00 +49.000000,1.082667000000e+00 +50.000000,1.041394000000e+00 +51.000000,1.005659000000e+00 +52.000000,9.744050000000e-01 +53.000000,9.465940000000e-01 +54.000000,9.212320000000e-01 +55.000000,8.974080000000e-01 +56.000000,8.743070000000e-01 +57.000000,8.512340000000e-01 +58.000000,8.276240000000e-01 +59.000000,8.030460000000e-01 +60.000000,7.772040000000e-01 +61.000000,7.499330000000e-01 +62.000000,7.211880000000e-01 +63.000000,6.910360000000e-01 +64.000000,6.596350000000e-01 +65.000000,6.272230000000e-01 +66.000000,5.940970000000e-01 +67.000000,5.605960000000e-01 +68.000000,5.270850000000e-01 +69.000000,4.939330000000e-01 +70.000000,4.615040000000e-01 +71.000000,4.301400000000e-01 +72.000000,4.001490000000e-01 +73.000000,3.717980000000e-01 +74.000000,3.453060000000e-01 +75.000000,3.208380000000e-01 +76.000000,2.985020000000e-01 +77.000000,2.783550000000e-01 +78.000000,2.603950000000e-01 +79.000000,2.445750000000e-01 +80.000000,2.308010000000e-01 +81.000000,2.189400000000e-01 +82.000000,2.088280000000e-01 +83.000000,2.002750000000e-01 +84.000000,1.930750000000e-01 +85.000000,1.870130000000e-01 +86.000000,1.818710000000e-01 +87.000000,1.774360000000e-01 +88.000000,1.735050000000e-01 +89.000000,1.698920000000e-01 +90.000000,1.664280000000e-01 +91.000000,1.629700000000e-01 +92.000000,1.594000000000e-01 +93.000000,1.556260000000e-01 +94.000000,1.515840000000e-01 +95.000000,1.472340000000e-01 +96.000000,1.425630000000e-01 +97.000000,1.375790000000e-01 +98.000000,1.323120000000e-01 +99.000000,1.268070000000e-01 +100.000000,1.211210000000e-01 +101.000000,1.153220000000e-01 +102.000000,1.094840000000e-01 +103.000000,1.036830000000e-01 +104.000000,9.799200000000e-02 +105.000000,9.248200000000e-02 +106.000000,8.721800000000e-02 +107.000000,8.225300000000e-02 +108.000000,7.763300000000e-02 +109.000000,7.339100000000e-02 +110.000000,6.954800000000e-02 +111.000000,6.611500000000e-02 +112.000000,6.308900000000e-02 +113.000000,6.045900000000e-02 +114.000000,5.820400000000e-02 +115.000000,5.629500000000e-02 +116.000000,5.469800000000e-02 +117.000000,5.337500000000e-02 +118.000000,5.228400000000e-02 +119.000000,5.138400000000e-02 +120.000000,5.063500000000e-02 +121.000000,5.000000000000e-02 +122.000000,4.944300000000e-02 +123.000000,4.893700000000e-02 +124.000000,4.845500000000e-02 +125.000000,4.797700000000e-02 +126.000000,4.749000000000e-02 +127.000000,4.698400000000e-02 +128.000000,4.645200000000e-02 +129.000000,4.589200000000e-02 +130.000000,4.530600000000e-02 +131.000000,4.469500000000e-02 +132.000000,4.406100000000e-02 +133.000000,4.340900000000e-02 +134.000000,4.273900000000e-02 +135.000000,4.205300000000e-02 +136.000000,4.135100000000e-02 +137.000000,4.062900000000e-02 +138.000000,3.988400000000e-02 +139.000000,3.910900000000e-02 +140.000000,3.830000000000e-02 +141.000000,3.744900000000e-02 +142.000000,3.655200000000e-02 +143.000000,3.560400000000e-02 +144.000000,3.460500000000e-02 +145.000000,3.355800000000e-02 +146.000000,3.247000000000e-02 +147.000000,3.135300000000e-02 +148.000000,3.022600000000e-02 +149.000000,2.911300000000e-02 +150.000000,2.804400000000e-02 +151.000000,2.705400000000e-02 +152.000000,2.618400000000e-02 +153.000000,2.547800000000e-02 +154.000000,2.498100000000e-02 +155.000000,2.474100000000e-02 +156.000000,2.480500000000e-02 +157.000000,2.521600000000e-02 +158.000000,2.601300000000e-02 +159.000000,2.722800000000e-02 +160.000000,2.888400000000e-02 +161.000000,3.099700000000e-02 +162.000000,3.356700000000e-02 +163.000000,3.658600000000e-02 +164.000000,4.003000000000e-02 +165.000000,4.386200000000e-02 +166.000000,4.803400000000e-02 +167.000000,5.248300000000e-02 +168.000000,5.713700000000e-02 +169.000000,6.191300000000e-02 +170.000000,6.672100000000e-02 +171.000000,7.146600000000e-02 +172.000000,7.605000000000e-02 +173.000000,8.037600000000e-02 +174.000000,8.434900000000e-02 +175.000000,8.787900000000e-02 +176.000000,9.088700000000e-02 +177.000000,9.330400000000e-02 +178.000000,9.507100000000e-02 +179.000000,9.614900000000e-02 +180.000000,9.651100000000e-02 diff --git a/tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.json b/tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.json new file mode 100644 index 00000000..c4684e70 --- /dev/null +++ b/tests/regression/frescox/reference/F10_p_ca48_pn_ias_35MeV.json @@ -0,0 +1,109 @@ +{ + "case_id": "F10_p_ca48_pn_ias_35MeV", + "reference_code": "frescox", + "observable_type": "quasielastic_pn", + "description": "Frescox-derived quasi-elastic 48Ca(p,n)48Sc(IAS) DWBA angular distribution at Elab(p) = 35.0 MeV. Deck contributed by Jin Lei (Tongji University); re-run locally. Independent of jitR and CHEX, so it checks the spin-flip Clebsch-Gordan terms.", + "source_example": "Ca48_pn_IAS_35MeV.in", + "reaction": { + "target": { + "A": 48, + "Z": 20, + "name": "48Ca" + }, + "projectile": { + "A": 1, + "Z": 1, + "name": "p" + }, + "product": { + "A": 1, + "Z": 0, + "name": "n" + }, + "residual": { + "A": 48, + "Z": 21, + "name": "48Sc" + } + }, + "mass_kwargs": { + "model": "BMA" + }, + "mass_model": "integer_amu", + "kinematics": { + "energy_MeV": 35.0, + "exit_energy_MeV": 27.67347917, + "frame": "lab", + "relativistic": false, + "Ecm_entrance_MeV": 34.28571429, + "Ecm_exit_MeV": 27.10871429, + "Q_MeV": -0.5, + "E_IAS_MeV": 6.677, + "comment": "Exit lab energy is taken from the deck rather than jitR's mass tables, so both codes see identical kinematics." + }, + "optical_potential": { + "kind": "woods_saxon_local_pn", + "scale_radii_by_At_and_Ap": false, + "proton": { + "V": 47.13547773, + "rv": 1.19234989, + "av": 0.6706624, + "W": 3.53838802, + "rw": 1.19234989, + "aw": 0.6706624, + "Wd": 6.85454469, + "rvd": 1.28479728, + "avd": 0.543684, + "Vso": 5.11263624, + "Wso": -0.2065247, + "rvso": 1.00737109, + "avso": 0.59 + }, + "neutron": { + "V": 42.02375462, + "rv": 1.19234989, + "av": 0.6706624, + "W": 2.492295, + "rw": 1.19234989, + "aw": 0.6706624, + "Wd": 5.63171244, + "rvd": 1.28479728, + "avd": 0.5366512, + "Vso": 5.21719662, + "Wso": -0.16293202, + "rvso": 1.00737109, + "avso": 0.59 + }, + "coulomb": { + "rC": 1.27126845 + }, + "comment": "KD02 (Koning-Delaroche 2003) local 13-parameter form, frozen at the entrance energy for p + (48,20) and the exit lab energy for n + (48,21), Thomas spin-orbit with (hbar/m_pi c)^2 = 2 fm^2." + }, + "transition_potential": { + "central": "default_isovector_difference", + "spin_orbit": "zero", + "comment": "U1_central is left to jitR's default, -(U_n - U_p) * sqrt|N-Z|/(N-Z-1), which equals the tabulated form factor the deck reads. A KIND=1 form factor cannot carry an l.s term, so U1_spin_orbit is zero on both sides.", + "form_factor_input": "tests/regression/frescox/inputs/Ca48_pn_IAS_35MeV.formfactor" + }, + "matching": { + "channel_radius_fm": 20.0, + "lmax": 40, + "nbasis": 60 + }, + "tolerances": { + "dsdo_mb_per_sr": { + "rtol": 0.001, + "atol": 1e-06 + } + }, + "reference_run": { + "code": "frescox", + "version": "7.2-20-ga7f491 (local build)", + "source_output": "tests/regression/frescox/outputs/Ca48_pn_IAS_35MeV.out", + "parser": "tests/regression/frescox/tools/parse_frescox.py", + "case_index": 1, + "min_angle_deg": 1.0, + "contributor": "Jin Lei, Tongji University", + "comment": "case_index 1 is the outgoing-neutron partition; block 0 is proton elastic. FSCALE = sqrt(2)*sqrt(4 pi) in the form-factor header is a Frescox convention that cancels against its KIND=1 coupling coefficient; see tools/make_pn_formfactor.py." + } +} diff --git a/tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.csv b/tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.csv new file mode 100644 index 00000000..b151e262 --- /dev/null +++ b/tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.csv @@ -0,0 +1,185 @@ +# case_id: F9_p_ca48_pn_ias_25MeV +# reference_code: frescox +# source_example: Ca48_pn_IAS_25MeV.in +# observable: quasielastic_pn +theta_cm_deg,dsdo_mb_per_sr +1.000000,2.444024000000e+00 +2.000000,2.449206000000e+00 +3.000000,2.457768000000e+00 +4.000000,2.469594000000e+00 +5.000000,2.484523000000e+00 +6.000000,2.502344000000e+00 +7.000000,2.522796000000e+00 +8.000000,2.545565000000e+00 +9.000000,2.570286000000e+00 +10.000000,2.596542000000e+00 +11.000000,2.623867000000e+00 +12.000000,2.651748000000e+00 +13.000000,2.679630000000e+00 +14.000000,2.706924000000e+00 +15.000000,2.733013000000e+00 +16.000000,2.757268000000e+00 +17.000000,2.779051000000e+00 +18.000000,2.797737000000e+00 +19.000000,2.812721000000e+00 +20.000000,2.823435000000e+00 +21.000000,2.829363000000e+00 +22.000000,2.830050000000e+00 +23.000000,2.825121000000e+00 +24.000000,2.814286000000e+00 +25.000000,2.797351000000e+00 +26.000000,2.774225000000e+00 +27.000000,2.744922000000e+00 +28.000000,2.709563000000e+00 +29.000000,2.668373000000e+00 +30.000000,2.621679000000e+00 +31.000000,2.569896000000e+00 +32.000000,2.513525000000e+00 +33.000000,2.453136000000e+00 +34.000000,2.389354000000e+00 +35.000000,2.322848000000e+00 +36.000000,2.254308000000e+00 +37.000000,2.184437000000e+00 +38.000000,2.113931000000e+00 +39.000000,2.043463000000e+00 +40.000000,1.973671000000e+00 +41.000000,1.905147000000e+00 +42.000000,1.838424000000e+00 +43.000000,1.773968000000e+00 +44.000000,1.712172000000e+00 +45.000000,1.653356000000e+00 +46.000000,1.597758000000e+00 +47.000000,1.545539000000e+00 +48.000000,1.496786000000e+00 +49.000000,1.451515000000e+00 +50.000000,1.409674000000e+00 +51.000000,1.371155000000e+00 +52.000000,1.335798000000e+00 +53.000000,1.303399000000e+00 +54.000000,1.273721000000e+00 +55.000000,1.246500000000e+00 +56.000000,1.221454000000e+00 +57.000000,1.198293000000e+00 +58.000000,1.176719000000e+00 +59.000000,1.156442000000e+00 +60.000000,1.137178000000e+00 +61.000000,1.118655000000e+00 +62.000000,1.100619000000e+00 +63.000000,1.082833000000e+00 +64.000000,1.065084000000e+00 +65.000000,1.047179000000e+00 +66.000000,1.028948000000e+00 +67.000000,1.010247000000e+00 +68.000000,9.909510000000e-01 +69.000000,9.709620000000e-01 +70.000000,9.502020000000e-01 +71.000000,9.286150000000e-01 +72.000000,9.061680000000e-01 +73.000000,8.828480000000e-01 +74.000000,8.586610000000e-01 +75.000000,8.336360000000e-01 +76.000000,8.078180000000e-01 +77.000000,7.812750000000e-01 +78.000000,7.540910000000e-01 +79.000000,7.263700000000e-01 +80.000000,6.982300000000e-01 +81.000000,6.698100000000e-01 +82.000000,6.412580000000e-01 +83.000000,6.127400000000e-01 +84.000000,5.844300000000e-01 +85.000000,5.565080000000e-01 +86.000000,5.291640000000e-01 +87.000000,5.025860000000e-01 +88.000000,4.769590000000e-01 +89.000000,4.524660000000e-01 +90.000000,4.292770000000e-01 +91.000000,4.075480000000e-01 +92.000000,3.874190000000e-01 +93.000000,3.690050000000e-01 +94.000000,3.523980000000e-01 +95.000000,3.376620000000e-01 +96.000000,3.248280000000e-01 +97.000000,3.138960000000e-01 +98.000000,3.048330000000e-01 +99.000000,2.975710000000e-01 +100.000000,2.920100000000e-01 +101.000000,2.880200000000e-01 +102.000000,2.854410000000e-01 +103.000000,2.840880000000e-01 +104.000000,2.837540000000e-01 +105.000000,2.842160000000e-01 +106.000000,2.852400000000e-01 +107.000000,2.865880000000e-01 +108.000000,2.880180000000e-01 +109.000000,2.892990000000e-01 +110.000000,2.902080000000e-01 +111.000000,2.905440000000e-01 +112.000000,2.901240000000e-01 +113.000000,2.887970000000e-01 +114.000000,2.864410000000e-01 +115.000000,2.829680000000e-01 +116.000000,2.783270000000e-01 +117.000000,2.725050000000e-01 +118.000000,2.655260000000e-01 +119.000000,2.574500000000e-01 +120.000000,2.483720000000e-01 +121.000000,2.384190000000e-01 +122.000000,2.277460000000e-01 +123.000000,2.165320000000e-01 +124.000000,2.049720000000e-01 +125.000000,1.932780000000e-01 +126.000000,1.816640000000e-01 +127.000000,1.703500000000e-01 +128.000000,1.595490000000e-01 +129.000000,1.494640000000e-01 +130.000000,1.402820000000e-01 +131.000000,1.321710000000e-01 +132.000000,1.252720000000e-01 +133.000000,1.197020000000e-01 +134.000000,1.155450000000e-01 +135.000000,1.128530000000e-01 +136.000000,1.116470000000e-01 +137.000000,1.119140000000e-01 +138.000000,1.136110000000e-01 +139.000000,1.166640000000e-01 +140.000000,1.209740000000e-01 +141.000000,1.264160000000e-01 +142.000000,1.328450000000e-01 +143.000000,1.401030000000e-01 +144.000000,1.480180000000e-01 +145.000000,1.564120000000e-01 +146.000000,1.651050000000e-01 +147.000000,1.739180000000e-01 +148.000000,1.826800000000e-01 +149.000000,1.912280000000e-01 +150.000000,1.994130000000e-01 +151.000000,2.071040000000e-01 +152.000000,2.141850000000e-01 +153.000000,2.205640000000e-01 +154.000000,2.261660000000e-01 +155.000000,2.309400000000e-01 +156.000000,2.348560000000e-01 +157.000000,2.379030000000e-01 +158.000000,2.400900000000e-01 +159.000000,2.414460000000e-01 +160.000000,2.420110000000e-01 +161.000000,2.418420000000e-01 +162.000000,2.410080000000e-01 +163.000000,2.395830000000e-01 +164.000000,2.376510000000e-01 +165.000000,2.352980000000e-01 +166.000000,2.326140000000e-01 +167.000000,2.296860000000e-01 +168.000000,2.266010000000e-01 +169.000000,2.234440000000e-01 +170.000000,2.202910000000e-01 +171.000000,2.172170000000e-01 +172.000000,2.142890000000e-01 +173.000000,2.115660000000e-01 +174.000000,2.091010000000e-01 +175.000000,2.069390000000e-01 +176.000000,2.051190000000e-01 +177.000000,2.036720000000e-01 +178.000000,2.026210000000e-01 +179.000000,2.019840000000e-01 +180.000000,2.017700000000e-01 diff --git a/tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.json b/tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.json new file mode 100644 index 00000000..fec28504 --- /dev/null +++ b/tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.json @@ -0,0 +1,109 @@ +{ + "case_id": "F9_p_ca48_pn_ias_25MeV", + "reference_code": "frescox", + "observable_type": "quasielastic_pn", + "description": "Frescox-derived quasi-elastic 48Ca(p,n)48Sc(IAS) DWBA angular distribution at Elab(p) = 25.0 MeV. Deck contributed by Jin Lei (Tongji University); re-run locally. Independent of jitR and CHEX, so it checks the spin-flip Clebsch-Gordan terms.", + "source_example": "Ca48_pn_IAS_25MeV.in", + "reaction": { + "target": { + "A": 48, + "Z": 20, + "name": "48Ca" + }, + "projectile": { + "A": 1, + "Z": 1, + "name": "p" + }, + "product": { + "A": 1, + "Z": 0, + "name": "n" + }, + "residual": { + "A": 48, + "Z": 21, + "name": "48Sc" + } + }, + "mass_kwargs": { + "model": "BMA" + }, + "mass_model": "integer_amu", + "kinematics": { + "energy_MeV": 25.0, + "exit_energy_MeV": 17.67347917, + "frame": "lab", + "relativistic": false, + "Ecm_entrance_MeV": 24.48979592, + "Ecm_exit_MeV": 17.31279592, + "Q_MeV": -0.5, + "E_IAS_MeV": 6.677, + "comment": "Exit lab energy is taken from the deck rather than jitR's mass tables, so both codes see identical kinematics." + }, + "optical_potential": { + "kind": "woods_saxon_local_pn", + "scale_radii_by_At_and_Ap": false, + "proton": { + "V": 50.96318785, + "rv": 1.19234989, + "av": 0.6706624, + "W": 2.29860499, + "rw": 1.19234989, + "aw": 0.6706624, + "Wd": 8.13772168, + "rvd": 1.28479728, + "avd": 0.543684, + "Vso": 5.32128688, + "Wso": -0.12462904, + "rvso": 1.00737109, + "avso": 0.59 + }, + "neutron": { + "V": 45.32118466, + "rv": 1.19234989, + "av": 0.6706624, + "W": 1.47531087, + "rw": 1.19234989, + "aw": 0.6706624, + "Wd": 6.52925801, + "rvd": 1.28479728, + "avd": 0.5366512, + "Vso": 5.43011446, + "Wso": -0.09011411, + "rvso": 1.00737109, + "avso": 0.59 + }, + "coulomb": { + "rC": 1.27126845 + }, + "comment": "KD02 (Koning-Delaroche 2003) local 13-parameter form, frozen at the entrance energy for p + (48,20) and the exit lab energy for n + (48,21), Thomas spin-orbit with (hbar/m_pi c)^2 = 2 fm^2." + }, + "transition_potential": { + "central": "default_isovector_difference", + "spin_orbit": "zero", + "comment": "U1_central is left to jitR's default, -(U_n - U_p) * sqrt|N-Z|/(N-Z-1), which equals the tabulated form factor the deck reads. A KIND=1 form factor cannot carry an l.s term, so U1_spin_orbit is zero on both sides.", + "form_factor_input": "tests/regression/frescox/inputs/Ca48_pn_IAS_25MeV.formfactor" + }, + "matching": { + "channel_radius_fm": 20.0, + "lmax": 40, + "nbasis": 60 + }, + "tolerances": { + "dsdo_mb_per_sr": { + "rtol": 0.001, + "atol": 1e-06 + } + }, + "reference_run": { + "code": "frescox", + "version": "7.2-20-ga7f491 (local build)", + "source_output": "tests/regression/frescox/outputs/Ca48_pn_IAS_25MeV.out", + "parser": "tests/regression/frescox/tools/parse_frescox.py", + "case_index": 1, + "min_angle_deg": 1.0, + "contributor": "Jin Lei, Tongji University", + "comment": "case_index 1 is the outgoing-neutron partition; block 0 is proton elastic. FSCALE = sqrt(2)*sqrt(4 pi) in the form-factor header is a Frescox convention that cancels against its KIND=1 coupling coefficient; see tools/make_pn_formfactor.py." + } +} diff --git a/tests/regression/frescox/tools/make_pn_formfactor.py b/tests/regression/frescox/tools/make_pn_formfactor.py new file mode 100644 index 00000000..14953544 --- /dev/null +++ b/tests/regression/frescox/tools/make_pn_formfactor.py @@ -0,0 +1,115 @@ +"""Write the Lane (p,n) transition form factor read by the Frescox IAS decks. + +The decks couple the p + 48Ca and n + 48Sc(IAS) partitions with a single local +``KIND=1`` form factor read from ``fort.4``. This writes that table, which is + + U1(r) = -(U_n^nuc(r) - U_p^nuc(r)) * sqrt(|N - Z|) / (N - Z - 1) + +built from the same KD02 central potentials as the decks, with their Coulomb +and spin-orbit parts removed. That is exactly ``U1_central`` of +:mod:`jitr.xs.quasielastic_pn`, so the regression case compares the same +operator on both sides. A ``KIND=1`` form factor cannot carry an ``l.s`` term, +which is why the case passes ``U1_spin_orbit = 0``. + +Two Frescox conventions are baked into the header: + +``FSCALE = sqrt(2) * sqrt(4 pi)`` + For a local ``KIND=1`` form factor with ``IP3=0``, ``INTER`` scales the + table by ``ASCALE = FSCALE * R4PI`` with ``R4PI = 1/sqrt(4 pi)`` + (``frxx7a.f``, ``globx7.f``), so the ``sqrt(4 pi)`` cancels ``R4PI``. The + remaining ``sqrt(2)`` is ``sqrt(2 j_p + 1)`` for the spin-1/2 projectile: + Frescox reads the table as a reduced matrix element, and the coupling + coefficient it multiplies (``frxx4.f``, ``IP3=0`` branch) is exactly + ``1/sqrt(2)`` for every ``(l, j)`` here. The two cancel, so Frescox's + matrix element is the plain ``U1`` and jitr needs no such factor. + +``LOP = DER = -1``, written explicitly + Frescox first reads the header expecting these two integers and only falls + back to the shorter form on an I/O error. That fallback re-reads after the + failed record, consuming the first data line, and the form factor is then + dropped **silently**: the (p,n) cross section comes out identically zero + with no error message. Writing them explicitly avoids the fallback. + +Usage:: + + uv run python tests/regression/frescox/tools/make_pn_formfactor.py \\ + --metadata tests/regression/frescox/reference/F9_p_ca48_pn_ias_25MeV.json \\ + --out tests/regression/frescox/inputs/Ca48_pn_IAS_25MeV.formfactor +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +FSCALE = np.sqrt(2.0) * np.sqrt(4.0 * np.pi) +LTR = PTR = TTR = 0 +IB = IA = 1 +LOP = DER = -1 + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--metadata", type=Path, required=True) + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--rmax-fm", type=float, default=20.0) + parser.add_argument("--step-fm", type=float, default=0.02) + return parser.parse_args() + + +def woods_saxon(r: np.ndarray, R: float, a: float) -> np.ndarray: + return 1.0 / (1.0 + np.exp((r - R) / a)) + + +def woods_saxon_derivative(r: np.ndarray, R: float, a: float) -> np.ndarray: + x = np.exp((r - R) / a) + return -x / (a * (1.0 + x) ** 2) + + +def central_potential(r: np.ndarray, p: dict[str, float], A: int) -> np.ndarray: + """Nuclear central KD02 potential: volume real, volume and surface imaginary.""" + A13 = A ** (1.0 / 3.0) + return ( + -p["V"] * woods_saxon(r, p["rv"] * A13, p["av"]) + - 1j * p["W"] * woods_saxon(r, p["rw"] * A13, p["aw"]) + - 1j + * p["Wd"] + * (-4.0 * p["avd"]) + * woods_saxon_derivative(r, p["rvd"] * A13, p["avd"]) + ) + + +def transition_potential(metadata: dict, r: np.ndarray) -> np.ndarray: + potential = metadata["optical_potential"] + A = int(metadata["reaction"]["target"]["A"]) + Z = int(metadata["reaction"]["target"]["Z"]) + N = A - Z + isovector_factor = np.sqrt(abs(N - Z)) / (N - Z - 1) + U_p = central_potential(r, potential["proton"], A) + U_n = central_potential(r, potential["neutron"], A) + return -(U_n - U_p) * isovector_factor + + +def write_formfactor(path: Path, U1: np.ndarray, step_fm: float) -> None: + header = ( + f"{U1.size:4d}{step_fm:8.4f}{0.0:8.4f}{FSCALE:8.4f}" + f"{LTR:4d}{PTR:4.0f}{TTR:4.0f}{IB:4d}{IA:4d}{LOP:4d}{DER:4d}" + "Lane U1 central" + ) + rows = [f"{value.real: .10e} {value.imag: .10e}" for value in U1] + path.write_text("\n".join([header, *rows]) + "\n") + + +def main() -> None: + args = parse_args() + metadata = json.loads(args.metadata.read_text()) + r = np.arange(0.0, args.rmax_fm + 0.5 * args.step_fm, args.step_fm) + write_formfactor(args.out, transition_potential(metadata, r), args.step_fm) + print(f"wrote {args.out} ({r.size} points to {r[-1]:.2f} fm)") + + +if __name__ == "__main__": + main() diff --git a/tests/regression/manifest.json b/tests/regression/manifest.json index 37f556c4..fbb296c3 100644 --- a/tests/regression/manifest.json +++ b/tests/regression/manifest.json @@ -47,6 +47,18 @@ "csv": "frescox/reference/F8_n_ni78_elastic_200MeV.csv", "json": "frescox/reference/F8_n_ni78_elastic_200MeV.json" }, + { + "case_id": "F9_p_ca48_pn_ias_25MeV", + "reference_code": "frescox", + "csv": "frescox/reference/F9_p_ca48_pn_ias_25MeV.csv", + "json": "frescox/reference/F9_p_ca48_pn_ias_25MeV.json" + }, + { + "case_id": "F10_p_ca48_pn_ias_35MeV", + "reference_code": "frescox", + "csv": "frescox/reference/F10_p_ca48_pn_ias_35MeV.csv", + "json": "frescox/reference/F10_p_ca48_pn_ias_35MeV.json" + }, { "case_id": "T1_jlm_elastic", "reference_code": "talys", diff --git a/tests/regression/test_regression.py b/tests/regression/test_regression.py index efbfeaa4..9804783f 100644 --- a/tests/regression/test_regression.py +++ b/tests/regression/test_regression.py @@ -10,9 +10,8 @@ def test_regression(case: ManifestEntry) -> None: """Compare one committed external reference against the current API.""" ref = load_case(case) built = build_case(ref) - result = built.workspace.xs(**built.xs_kwargs) np.testing.assert_allclose( - result.dsdo, + built.dsdo(), ref.dsdo, rtol=ref.tolerance["rtol"], atol=ref.tolerance["atol"], From 5ab7046b117c4ad3e02b626406097b8733f9f40c Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 24 Sep 2026 18:05:03 -0400 Subject: [PATCH 08/11] add Ay for (p,n) with examples --- docs/examples/index.md | 4 + examples/notebooks/qepn_analyzing_power.ipynb | 1073 +++++++++++++++++ src/jitr/xs/lane_pn.py | 71 +- src/jitr/xs/quasielastic_pn.py | 131 +- tests/test_pn_analyzing_power.py | 218 ++++ 5 files changed, 1492 insertions(+), 5 deletions(-) create mode 100644 examples/notebooks/qepn_analyzing_power.ipynb create mode 100644 tests/test_pn_analyzing_power.py diff --git a/docs/examples/index.md b/docs/examples/index.md index 38a30aa7..e65e1e47 100644 --- a/docs/examples/index.md +++ b/docs/examples/index.md @@ -16,6 +16,7 @@ Every notebook also runs in [Google Colab](https://colab.research.google.com/): /examples/notebooks/example_jlm /examples/notebooks/kd03_neutron_dxs_demo /examples/notebooks/builtin_omps_uq +/examples/notebooks/qepn_analyzing_power /examples/notebooks/tabulated_density_demo /examples/notebooks/chuq_kduq_comp /examples/notebooks/volume_integrals @@ -54,6 +55,9 @@ Every notebook also runs in [Google Colab](https://colab.research.google.com/): walks through posterior sampling, solver setup, and interval construction for several of the built-in uncertainty quantified optical potentials in `jitr`. [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/beykyle/jitr/blob/main/examples/notebooks/builtin_omps_uq.ipynb) +- [Analyzing power of the quasi-elastic (p,n) reaction](/examples/notebooks/qepn_analyzing_power) + pulls nine angular distributions from EXFOR, solves the coupled Lane channels for the charge-exchange transition to the isobaric analog state, and propagates the KDUQ, WLH and CHUQ posteriors through to the analyzing power, an observable that depends almost entirely on the isovector spin-orbit potential. + [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/beykyle/jitr/blob/main/examples/notebooks/qepn_analyzing_power.ipynb) - [Tabulated neutron and proton densities](/examples/notebooks/tabulated_density_demo) shows how to grab the tabulated nuclear densities in `jitr`. [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/beykyle/jitr/blob/main/examples/notebooks/tabulated_density_demo.ipynb) diff --git a/examples/notebooks/qepn_analyzing_power.ipynb b/examples/notebooks/qepn_analyzing_power.ipynb new file mode 100644 index 00000000..015efd8a --- /dev/null +++ b/examples/notebooks/qepn_analyzing_power.ipynb @@ -0,0 +1,1073 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ba38fa7e", + "metadata": {}, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/beykyle/jitr/blob/main/examples/notebooks/qepn_analyzing_power.ipynb)\n", + "\n", + "On Colab the first `import exfor_tools` downloads and unpacks the EXFOR database, which takes a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "135b5879", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:22.824184Z", + "iopub.status.busy": "2026-09-24T20:01:22.824049Z", + "iopub.status.idle": "2026-09-24T20:01:22.831715Z", + "shell.execute_reply": "2026-09-24T20:01:22.830825Z" + } + }, + "outputs": [], + "source": [ + "# Google Colab setup: installs jitr and this notebook's extra dependencies.\n", + "# Does nothing when the notebook is run anywhere else.\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " import subprocess\n", + "\n", + " subprocess.run(\n", + " [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"jitr\", \"exfor-tools\"],\n", + " check=True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "204d393d", + "metadata": {}, + "source": [ + "# Analyzing power of the quasi-elastic $(\\vec{p},n)$ reaction\n", + "\n", + "In 1976 Gosset, Mayer and Escudié measured the differential analyzing power $A_y(\\theta)$ of\n", + "the quasi-elastic $(p,n)$ reaction to the isobaric analog state (IAS) on nine targets, with a\n", + "22.8 MeV polarized proton beam at Saclay\n", + "([Phys. Rev. C **14**, 878](https://doi.org/10.1103/PhysRevC.14.878)).\n", + "Those data are EXFOR entry [O0669](https://www-nds.iaea.org/exfor/servlet/X4sGetSubent?reqx=51740&subID=O0669).\n", + "\n", + "Their point was that $A_y$ is a far sharper probe of the Lane potential than the cross section is.\n", + "In the macroscopic Lane model the nucleon-nucleus optical potential splits into isoscalar and\n", + "isovector parts,\n", + "\n", + "$$ U = U_0 + \\frac{4}{A} U_1\\, \\vec{t} \\cdot \\vec{T}, $$\n", + "\n", + "which for the proton-target, neutron-analog and neutron-target channels gives\n", + "\n", + "$$ U_{pT} = U_0 - \\frac{2 T_0}{A} U_1, \\qquad\n", + " U_{nA} = U_0 + \\frac{2 (T_0 - 1)}{A} U_1, \\qquad\n", + " U_{nT} = U_0 + \\frac{2 T_0}{A} U_1, $$\n", + "\n", + "with $T_0 = (N - Z)/2$. The off-diagonal part of $U_1$ drives the charge-exchange transition\n", + "between $|T_0 T_0\\rangle$ and its analog $|T_0, T_0 - 1\\rangle$. Because the transition carries\n", + "$l = s = j = 0$, only two amplitudes survive — one conserving and one inverting the projectile\n", + "spin projection — and the analyzing power is\n", + "\n", + "$$ A_y = \\frac{2\\,\\mathrm{Im}(Y X^*)}{|X|^2 + |Y|^2}. $$\n", + "\n", + "The spin-flip amplitude $Y$ is built from the *difference* between the $j = l + \\tfrac{1}{2}$ and\n", + "$j = l - \\tfrac{1}{2}$ radial matrix elements. If the entrance and exit distorting spin-orbit\n", + "potentials both vanish, those matrix elements stop depending on $j$, $Y$ vanishes, and\n", + "$A_y \\equiv 0$ identically. So $A_y$ measures spin-orbit physics and almost nothing else — which\n", + "is why Gosset *et al.* used it to try to pin down the isovector spin-orbit depth $V^{(1)}_{so}$.\n", + "\n", + "This notebook solves the two coupled Lane channels exactly with\n", + "[`jitr.xs.lane_pn`](https://jitr.readthedocs.io/en/latest/autoapi/jitr/xs/lane_pn/index.html)\n", + "rather than in DWBA, and propagates the posteriors of three uncertainty-quantified global optical\n", + "potentials — KDUQ, WLH and CHUQ — plus a deterministic JLMB calculation, through to $A_y$ for all\n", + "nine targets.\n", + "\n", + "A note on the targets: $^{49}$Ti, $^{117}$Sn and $^{165}$Ho have non-zero ground-state spin, but the\n", + "transition transfers no angular momentum, so the target spin is a spectator and the spin-1/2\n", + "geometry used here still applies." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d732cdc9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:22.833074Z", + "iopub.status.busy": "2026-09-24T20:01:22.832914Z", + "iopub.status.idle": "2026-09-24T20:01:24.631476Z", + "shell.execute_reply": "2026-09-24T20:01:24.630646Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import periodictable\n", + "\n", + "import jitr\n", + "from jitr.folding import ILDAFolder\n", + "from jitr.folding.jlm import (\n", + " lambda_v0,\n", + " lambda_v1,\n", + " lambda_vso,\n", + " lambda_w0,\n", + " lambda_w1,\n", + " lambda_wso,\n", + " potential_JLMB,\n", + " spin_orbit_jlmb,\n", + ")\n", + "from jitr.optical_potentials import chuq, kduq, wlh\n", + "from jitr.utils.density import density_table\n", + "from jitr.xs import lane_pn, quasielastic_pn\n", + "\n", + "# set JITR_QUICK=1 to thin the posterior draws; used by the CI notebook run\n", + "QUICK = os.environ.get(\"JITR_QUICK\", \"0\") == \"1\"" + ] + }, + { + "cell_type": "markdown", + "id": "ef5ca7f2", + "metadata": {}, + "source": [ + "## Pulling the measurements from EXFOR\n", + "\n", + "Each of the nine targets is a subentry of O0669, tabulated as an analyzing power against the\n", + "center-of-mass angle, together with the excitation energy of the analog state in the residual\n", + "nucleus. That excitation energy is what fixes the outgoing neutron energy, so we read it from the\n", + "entry rather than hard-coding it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8d1eb7d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:24.633068Z", + "iopub.status.busy": "2026-09-24T20:01:24.632870Z", + "iopub.status.idle": "2026-09-24T20:01:25.618763Z", + "shell.execute_reply": "2026-09-24T20:01:25.618072Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@article{JGosset1976_O0669001,\n", + " author = {J.Gosset and B.Mayer and J.L.Escudie},\n", + " title = {Quasielastic (P,N) Reactions Induced By Polarized Protons.},\n", + " journal = {Physical Review, Part C, Nuclear Physics},\n", + " year = {1976},\n", + " volume = {14},\n", + " pages = {878},\n", + " note = {EXFOR reference: Physical Review, Part C, Nuclear Physics 14, 878 (1976); EXFOR subent: O0669001}\n", + "}\n" + ] + } + ], + "source": [ + "from exfor_tools import ExforEntry\n", + "from exfor_tools.parsing import quantity_matches, quantity_symbols\n", + "from exfor_tools.reaction import Reaction as ExforReaction\n", + "\n", + "# exfor-tools <= 1.2 maps \"Ay\" only to [\"POL/DA\", \"ANA\"], but EXFOR qualifies a measurement\n", + "# resolved to a particular level of the residual with \"PAR\" -- as it must for a transition to\n", + "# the analog state. Fixed upstream, so this is a no-op against a newer exfor-tools.\n", + "par_ay = [\"PAR\", \"POL/DA\", \"ANA\"]\n", + "if par_ay not in quantity_matches[\"Ay\"]:\n", + " quantity_matches[\"Ay\"].append(par_ay)\n", + " quantity_symbols[tuple(par_ay)] = r\"$A_y$\"\n", + "\n", + "# the nine targets of Table I, in the order the paper presents them\n", + "TARGETS = [\n", + " (49, 22),\n", + " (56, 26),\n", + " (64, 28),\n", + " (70, 30),\n", + " (90, 40),\n", + " (96, 40),\n", + " (117, 50),\n", + " (165, 67),\n", + " (208, 82),\n", + "]\n", + "PROTON, NEUTRON = (1, 1), (1, 0)\n", + "\n", + "\n", + "def nuclide(A, Z):\n", + " return rf\"$^{{{A}}}${periodictable.elements[Z].symbol}\"\n", + "\n", + "\n", + "datasets = []\n", + "for A, Z in TARGETS:\n", + " entry = ExforEntry(\n", + " \"O0669\",\n", + " ExforReaction(\n", + " target=(A, Z), projectile=PROTON, product=NEUTRON, residual=(A, Z + 1)\n", + " ),\n", + " quantity=\"Ay\",\n", + " )\n", + " (measurement,) = entry.measurements\n", + " datasets.append(\n", + " {\n", + " \"A\": A,\n", + " \"Z\": Z,\n", + " \"name\": f\"{A}{periodictable.elements[Z].symbol}\",\n", + " \"residual_name\": f\"{A}{periodictable.elements[Z + 1].symbol}\",\n", + " \"label\": nuclide(A, Z),\n", + " \"subentry\": measurement.subentry,\n", + " \"E_lab\": measurement.Einc,\n", + " \"E_IAS\": measurement.Ex,\n", + " \"theta\": measurement.x,\n", + " \"Ay\": measurement.y,\n", + " \"Ay_err\": measurement.statistical_err,\n", + " }\n", + " )\n", + "\n", + "print(entry.bibtex())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1d3f5bd4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:25.620297Z", + "iopub.status.busy": "2026-09-24T20:01:25.620045Z", + "iopub.status.idle": "2026-09-24T20:01:25.624263Z", + "shell.execute_reply": "2026-09-24T20:01:25.623468Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " target residual subentry E_IAS [MeV] points angular range [deg]\n", + "--------------------------------------------------------------------------\n", + " 49Ti 49V O0669002 5.36 18 16.6 - 103.2\n", + " 56Fe 56Co O0669003 3.51 23 15.7 - 125.1\n", + " 64Ni 64Cu O0669004 6.70 19 16.7 - 106.4\n", + " 70Zn 70Ga O0669005 8.12 20 16.0 - 114.3\n", + " 90Zr 90Nb O0669006 5.03 22 15.4 - 124.3\n", + " 96Zr 96Nb O0669007 11.07 18 14.4 - 98.7\n", + " 117Sn 117Sb O0669008 11.18 17 15.0 - 94.9\n", + " 165Ho 165Er O0669009 15.49 8 16.9 - 61.0\n", + " 208Pb 208Bi O0669010 15.33 4 17.3 - 31.4\n", + "\n", + "149 points, all at E_p = 22.8 MeV\n" + ] + } + ], + "source": [ + "header = f\"{'target':>8} {'residual':>10} {'subentry':>10} {'E_IAS [MeV]':>12} {'points':>7} {'angular range [deg]':>22}\"\n", + "print(header)\n", + "print(\"-\" * len(header))\n", + "for d in datasets:\n", + " angles = f\"{d['theta'][0]:.1f} - {d['theta'][-1]:.1f}\"\n", + " print(\n", + " f\"{d['name']:>8} {d['residual_name']:>10} {d['subentry']:>10} \"\n", + " f\"{d['E_IAS']:>12.2f} {len(d['theta']):>7} {angles:>22}\"\n", + " )\n", + "print(\n", + " f\"\\n{sum(len(d['theta']) for d in datasets)} points, all at E_p = {datasets[0]['E_lab']} MeV\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8b46aad9", + "metadata": {}, + "source": [ + "## Building one coupled-channels workspace per target\n", + "\n", + "`jitr.xs.lane_pn.Workspace` solves the two-channel problem\n", + "\n", + "$$ \\left[T_l + U_{pp} - E_p\\right] u_p + U_1 u_n = 0, \\qquad\n", + " \\left[T_l + U_{nn} - E_n\\right] u_n + U_1 u_p = 0 $$\n", + "\n", + "for every $(l, j)$, with an incoming wave in the proton channel only. The two channels have\n", + "different wavenumbers, reduced masses and Sommerfeld parameters but share one physical channel\n", + "radius, and the R-matrix solution gives the full $2 \\times 2$ S-matrix, whose off-diagonal element\n", + "is the charge-exchange amplitude.\n", + "\n", + "The exit channel needs its own `Reaction` so that the neutron optical potential is evaluated on the\n", + "*residual* nucleus rather than on the target. Building the solvers and workspaces in their own cell\n", + "keeps the numba compilation and the Lagrange-mesh precompute out of the sample loop below." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "026ba475", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:25.625634Z", + "iopub.status.busy": "2026-09-24T20:01:25.625490Z", + "iopub.status.idle": "2026-09-24T20:01:27.109730Z", + "shell.execute_reply": "2026-09-24T20:01:27.108973Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 49Ti E_n = 15.92 MeV a = 10.47 fm nbasis = 20\n", + " 56Fe E_n = 13.78 MeV a = 10.66 fm nbasis = 20\n", + " 64Ni E_n = 13.50 MeV a = 10.85 fm nbasis = 20\n", + " 70Zn E_n = 13.11 MeV a = 10.99 fm nbasis = 20\n", + " 90Zr E_n = 10.74 MeV a = 11.40 fm nbasis = 20\n", + " 96Zr E_n = 10.99 MeV a = 11.52 fm nbasis = 20\n", + " 117Sn E_n = 8.96 MeV a = 11.88 fm nbasis = 20\n", + " 165Ho E_n = 6.05 MeV a = 12.58 fm nbasis = 25\n", + " 208Pb E_n = 3.72 MeV a = 13.10 fm nbasis = 25\n" + ] + } + ], + "source": [ + "E_LAB = 22.8\n", + "LMAX = 25\n", + "ANGLES = np.radians(np.linspace(1.0, 180.0, 180))\n", + "\n", + "solvers = {}\n", + "for d in datasets:\n", + " A, Z = d[\"A\"], d[\"Z\"]\n", + " reaction = jitr.reactions.Reaction(\n", + " target=(A, Z), projectile=PROTON, product=NEUTRON, residual=(A, Z + 1)\n", + " )\n", + " # \"El\" so that the neutron potential is evaluated for n + residual\n", + " exit_reaction = jitr.reactions.Reaction(\n", + " target=(A, Z + 1), projectile=NEUTRON, process=\"El\"\n", + " )\n", + " kinematics_entrance = reaction.kinematics(E_LAB)\n", + " kinematics_exit = reaction.kinematics_exit(\n", + " kinematics_entrance, residual_excitation_energy=d[\"E_IAS\"]\n", + " )\n", + "\n", + " a = jitr.utils.interaction_range(A) * kinematics_entrance.k + 2 * np.pi\n", + " nbasis = jitr.utils.suggested_basis_size(a)\n", + " solver = solvers.setdefault(nbasis, jitr.rmatrix.Solver(nbasis))\n", + "\n", + " d.update(\n", + " reaction=reaction,\n", + " exit_reaction=exit_reaction,\n", + " kinematics_entrance=kinematics_entrance,\n", + " kinematics_exit=kinematics_exit,\n", + " nbasis=nbasis,\n", + " channel_radius_fm=a / kinematics_entrance.k,\n", + " workspace=lane_pn.Workspace(\n", + " reaction,\n", + " kinematics_entrance,\n", + " kinematics_exit,\n", + " solver,\n", + " ANGLES,\n", + " LMAX,\n", + " a / kinematics_entrance.k,\n", + " ),\n", + " )\n", + "\n", + "for d in datasets:\n", + " print(\n", + " f\"{d['name']:>8} E_n = {d['kinematics_exit'].Elab:5.2f} MeV \"\n", + " f\"a = {d['channel_radius_fm']:5.2f} fm nbasis = {d['nbasis']:2d}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "44521e19", + "metadata": {}, + "source": [ + "## Checking convergence\n", + "\n", + "The isovector coupling is weak, so the charge-exchange S-matrix element falls off quickly with $l$\n", + "and the calculation converges at modest $l_{max}$ and basis size. Doubling $l_{max}$ and enlarging\n", + "the channel radius move $A_y$ by less than $4 \\times 10^{-3}$ in absolute terms, on a quantity\n", + "bounded by 1 — though note that near a zero crossing that is not a small *relative* shift, and this\n", + "checks one target rather than all nine. The distorting potentials here are the original frequentist\n", + "Koning-Delaroche parameters; the posterior draws below are no harder to converge." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c1a51695", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:27.111114Z", + "iopub.status.busy": "2026-09-24T20:01:27.110994Z", + "iopub.status.idle": "2026-09-24T20:01:34.220520Z", + "shell.execute_reply": "2026-09-24T20:01:34.219926Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56Fe, relative to lmax=25, a=10.66 fm, nbasis=20\n", + " lmax= 25 a=10.66 fm nbasis= 30 max |dAy| = 3.77e-03\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lmax= 40 a=10.66 fm nbasis= 20 max |dAy| = 0.00e+00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lmax= 40 a=16.00 fm nbasis= 35 max |dAy| = 3.90e-03\n" + ] + } + ], + "source": [ + "reference = datasets[1] # 56Fe\n", + "omp_p, omp_n = kduq.KDUQ(PROTON), kduq.KDUQ(NEUTRON)\n", + "# the original frequentist Koning-Delaroche set, not a central value of the KDUQ posterior\n", + "params_p = kduq.get_kd03(PROTON)\n", + "params_n = kduq.get_kd03(NEUTRON)\n", + "\n", + "\n", + "def ay_at(lmax, channel_radius_fm, nbasis):\n", + " workspace = lane_pn.Workspace(\n", + " reference[\"reaction\"],\n", + " reference[\"kinematics_entrance\"],\n", + " reference[\"kinematics_exit\"],\n", + " jitr.rmatrix.Solver(nbasis),\n", + " ANGLES,\n", + " lmax,\n", + " channel_radius_fm,\n", + " )\n", + " rgrid = workspace.radial_grid()\n", + " U_p, U_p_so, U_coulomb = omp_p(\n", + " rgrid, reference[\"reaction\"], reference[\"kinematics_entrance\"], *params_p\n", + " )\n", + " U_n, U_n_so, _ = omp_n(\n", + " rgrid, reference[\"exit_reaction\"], reference[\"kinematics_exit\"], *params_n\n", + " )\n", + " return workspace.observables(U_coulomb, U_p, U_p_so, U_n, U_n_so).Ay\n", + "\n", + "\n", + "baseline = ay_at(LMAX, reference[\"channel_radius_fm\"], reference[\"nbasis\"])\n", + "print(\n", + " f\"{reference['name']}, relative to lmax={LMAX}, a={reference['channel_radius_fm']:.2f} fm, nbasis={reference['nbasis']}\"\n", + ")\n", + "for lmax, a_fm, nbasis in [\n", + " (LMAX, reference[\"channel_radius_fm\"], reference[\"nbasis\"] + 10),\n", + " (40, reference[\"channel_radius_fm\"], reference[\"nbasis\"]),\n", + " (40, 16.0, 35),\n", + "]:\n", + " shift = np.max(np.absolute(ay_at(lmax, a_fm, nbasis) - baseline))\n", + " print(\n", + " f\" lmax={lmax:3d} a={a_fm:5.2f} fm nbasis={nbasis:3d} max |dAy| = {shift:.2e}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "76ec66a9", + "metadata": {}, + "source": [ + "## Propagating the optical model posteriors\n", + "\n", + "KDUQ, WLH and CHUQ each ship a posterior sample of their parameter vectors, so a predictive band\n", + "for $A_y$ is one solve per draw. Nothing here is a fit to these data: the potentials were\n", + "calibrated to elastic scattering, and the Lane transition potential is taken to be the default\n", + "isovector difference\n", + "\n", + "$$ U_1 = -\\left(U_n - U_p\\right) \\frac{\\sqrt{|N - Z|}}{N - Z - 1}, $$\n", + "\n", + "evaluated separately for the central and spin-orbit terms — so the isovector spin-orbit form factor\n", + "that the analyzing power is sensitive to comes entirely from the difference between the proton and\n", + "neutron spin-orbit potentials of each global parameterization.\n", + "\n", + "CHUQ deserves a word: CH89 is Lane-consistent by construction, so it is the one built-in whose\n", + "proton and neutron potentials really are the $U_{pT}$ and $U_{nA}$ of the equations above, and\n", + "whose difference therefore really is $U_1$. For KDUQ and WLH the same construction is an\n", + "approximation, and the diagonal potentials may already absorb part of the coupling." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eb337795", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:01:34.226979Z", + "iopub.status.busy": "2026-09-24T20:01:34.226796Z", + "iopub.status.idle": "2026-09-24T20:02:43.145414Z", + "shell.execute_reply": "2026-09-24T20:02:43.144610Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kyle/umich/jitr/src/jitr/optical_potentials/kduq.py:470: RuntimeWarning: overflow encountered in exp\n", + " d2 = d2_0 + d2_A / (1 + np.exp((A - d2_A3) / d2_A2))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KDUQ: 416 posterior draws x 9 targets\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WLH: 1000 posterior draws x 9 targets\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHUQ: 208 posterior draws x 9 targets\n" + ] + } + ], + "source": [ + "OMPS = {\n", + " \"KDUQ\": (\n", + " kduq.KDUQ(PROTON),\n", + " kduq.KDUQ(NEUTRON),\n", + " kduq.get_samples(PROTON),\n", + " kduq.get_samples(NEUTRON),\n", + " ),\n", + " \"WLH\": (\n", + " wlh.WLH(PROTON),\n", + " wlh.WLH(NEUTRON),\n", + " wlh.get_samples(PROTON),\n", + " wlh.get_samples(NEUTRON),\n", + " ),\n", + " # CH89 is Lane consistent: one parameter set serves both projectiles\n", + " \"CHUQ\": (chuq.CHUQ(), chuq.CHUQ(), chuq.get_samples(), chuq.get_samples()),\n", + "}\n", + "\n", + "\n", + "def propagate(omp_p, omp_n, samples_p, samples_n):\n", + " n_draws = min(len(samples_p), len(samples_n))\n", + " if QUICK:\n", + " n_draws = min(n_draws, 50)\n", + "\n", + " bands = []\n", + " for d in datasets:\n", + " workspace = d[\"workspace\"]\n", + " rgrid = workspace.radial_grid()\n", + " Ay = np.zeros((len(ANGLES), n_draws))\n", + " dsdo = np.zeros((len(ANGLES), n_draws))\n", + " for j in range(n_draws):\n", + " U_p, U_p_so, U_coulomb = omp_p(\n", + " rgrid, d[\"reaction\"], d[\"kinematics_entrance\"], *samples_p[j]\n", + " )\n", + " U_n, U_n_so, _ = omp_n(\n", + " rgrid, d[\"exit_reaction\"], d[\"kinematics_exit\"], *samples_n[j]\n", + " )\n", + " _, S = workspace.rsmatrix(U_coulomb, U_p, U_p_so, U_n, U_n_so)\n", + " observables = workspace.observables_from_smatrix(S)\n", + " Ay[:, j] = observables.Ay\n", + " dsdo[:, j] = observables.dsdo\n", + " bands.append(\n", + " {\n", + " \"Ay\": np.percentile(Ay, [16, 84], axis=1),\n", + " \"Ay_median\": np.median(Ay, axis=1),\n", + " \"dsdo\": np.percentile(dsdo, [16, 84], axis=1),\n", + " \"dsdo_median\": np.median(dsdo, axis=1),\n", + " }\n", + " )\n", + " return n_draws, bands\n", + "\n", + "\n", + "predictions = {}\n", + "for name, args in OMPS.items():\n", + " n_draws, bands = propagate(*args)\n", + " predictions[name] = bands\n", + " print(f\"{name}: {n_draws} posterior draws x {len(datasets)} targets\")" + ] + }, + { + "cell_type": "markdown", + "id": "cdc096da", + "metadata": {}, + "source": [ + "## A semi-microscopic comparison: JLMB\n", + "\n", + "JLMB is a different kind of potential: a local-density folding of the Jeukenne-Lejeune-Mahaux\n", + "nuclear-matter *g*-matrix over a Gogny D1M density, with energy-dependent normalizations\n", + "$\\lambda_V, \\lambda_W, \\lambda_{V_1}, \\lambda_{W_1}$ and a Scheerbaum spin-orbit form factor. It is\n", + "the one potential here with a genuine isovector form factor rather than one obtained by\n", + "differencing — but it ships no posterior sample, so what follows is a single curve, not a band." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "36737bf1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:02:43.147195Z", + "iopub.status.busy": "2026-09-24T20:02:43.147058Z", + "iopub.status.idle": "2026-09-24T20:02:43.321502Z", + "shell.execute_reply": "2026-09-24T20:02:43.320803Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JLMB evaluated for 9 targets\n" + ] + } + ], + "source": [ + "def jlmb_potentials(d):\n", + " workspace = d[\"workspace\"]\n", + " rgrid = workspace.radial_grid()\n", + " folder = ILDAFolder(r_max=15.0, n_quad=200)\n", + "\n", + " def channel(A, Z, projectile, energy, coulomb):\n", + " table = density_table(A, Z, model=\"d1m\")\n", + " rho_n = folder.interp_to_quad(table.radial_grid, table.neutron_density_grid)\n", + " rho_p = folder.interp_to_quad(table.radial_grid, table.proton_density_grid)\n", + " V_C_quad = folder.V_coulomb(rho_p) if coulomb else None\n", + " V, W = potential_JLMB(\n", + " folder,\n", + " rho_n,\n", + " rho_p,\n", + " projectile,\n", + " (A, Z),\n", + " energy,\n", + " V_C=V_C_quad,\n", + " parameterization=\"talys\",\n", + " lambda_V=float(lambda_v0(energy)),\n", + " lambda_W=float(lambda_w0(energy, mode=0)),\n", + " lambda_V1=float(lambda_v1(energy)),\n", + " lambda_W1=float(lambda_w1(energy, mode=0)),\n", + " t_r=1.25,\n", + " t_i=1.35,\n", + " r_out=rgrid,\n", + " )\n", + " spin_orbit = (\n", + " float(lambda_vso(energy)) + 1j * float(lambda_wso(energy))\n", + " ) * spin_orbit_jlmb(folder.r_q, rho_n, rho_p, projectile, r_out=rgrid)\n", + " coulomb_out = folder.V_coulomb(rho_p, r_out=rgrid) if coulomb else None\n", + " return (\n", + " np.asarray(V + 1j * W, dtype=np.complex128),\n", + " np.asarray(spin_orbit, dtype=np.complex128),\n", + " (\n", + " None\n", + " if coulomb_out is None\n", + " else np.asarray(coulomb_out, dtype=np.complex128)\n", + " ),\n", + " )\n", + "\n", + " U_p, U_p_so, U_coulomb = channel(\n", + " d[\"A\"], d[\"Z\"], PROTON, d[\"kinematics_entrance\"].Elab, coulomb=True\n", + " )\n", + " U_n, U_n_so, _ = channel(\n", + " d[\"A\"], d[\"Z\"] + 1, NEUTRON, d[\"kinematics_exit\"].Elab, coulomb=False\n", + " )\n", + " return U_coulomb, U_p, U_p_so, U_n, U_n_so\n", + "\n", + "\n", + "jlmb = []\n", + "for d in datasets:\n", + " jlmb.append(d[\"workspace\"].observables(*jlmb_potentials(d)))\n", + "print(f\"JLMB evaluated for {len(jlmb)} targets\")" + ] + }, + { + "cell_type": "markdown", + "id": "520dc615", + "metadata": {}, + "source": [ + "## The measurements against the calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f58c307d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:02:43.324364Z", + "iopub.status.busy": "2026-09-24T20:02:43.324160Z", + "iopub.status.idle": "2026-09-24T20:02:44.433565Z", + "shell.execute_reply": "2026-09-24T20:02:44.432894Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "BANDS = {\"KDUQ\": \"#0072B2\", \"WLH\": \"#D55E00\", \"CHUQ\": \"#009E73\"}\n", + "JLMB_INK = \"#333333\"\n", + "angles_deg = np.degrees(ANGLES)\n", + "\n", + "\n", + "def grid_figure(ylabel):\n", + " fig, axes = plt.subplots(3, 3, figsize=(12, 10), sharex=True)\n", + " fig.subplots_adjust(hspace=0.12, wspace=0.22)\n", + " for ax in axes[-1]:\n", + " ax.set_xlabel(r\"$\\theta_{\\rm c.m.}$ [deg]\")\n", + " for ax in axes[:, 0]:\n", + " ax.set_ylabel(ylabel)\n", + " for ax in axes.flat:\n", + " ax.set_xlim(0, 140)\n", + " ax.tick_params(direction=\"in\", top=True, right=True)\n", + " for side in ax.spines.values():\n", + " side.set_linewidth(0.8)\n", + " return fig, axes\n", + "\n", + "\n", + "fig, axes = grid_figure(r\"$A_y$\")\n", + "for i, (ax, d, curve) in enumerate(zip(axes.flat, datasets, jlmb, strict=True)):\n", + " for name, color in BANDS.items():\n", + " lo, hi = predictions[name][i][\"Ay\"]\n", + " ax.fill_between(angles_deg, lo, hi, color=color, alpha=0.35, lw=0, label=name)\n", + " ax.plot(angles_deg, predictions[name][i][\"Ay_median\"], color=color, lw=1.2)\n", + " ax.plot(angles_deg, curve.Ay, color=JLMB_INK, lw=1.2, ls=\"--\", label=\"JLMB\")\n", + " ax.errorbar(\n", + " d[\"theta\"],\n", + " d[\"Ay\"],\n", + " yerr=d[\"Ay_err\"],\n", + " fmt=\"o\",\n", + " ms=3.5,\n", + " color=\"k\",\n", + " lw=0.9,\n", + " capsize=1.5,\n", + " label=\"Gosset (1976)\",\n", + " )\n", + " ax.axhline(0.0, color=\"0.75\", lw=0.7, zorder=0)\n", + " ax.set_ylim(-1.05, 1.05)\n", + " ax.text(0.04, 0.06, d[\"label\"], transform=ax.transAxes, fontsize=13)\n", + "\n", + "axes[0, 0].legend(loc=\"upper right\", fontsize=8, frameon=False, ncols=2)\n", + "fig.suptitle(r\"$(\\vec{p},n)$ analyzing power to the IAS at $E_p = 22.8$ MeV\", y=0.92)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "26d48672", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:02:44.436039Z", + "iopub.status.busy": "2026-09-24T20:02:44.435838Z", + "iopub.status.idle": "2026-09-24T20:02:44.443206Z", + "shell.execute_reply": "2026-09-24T20:02:44.442568Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " theta_1 | Ay(theta_1) | for theta > 40 deg\n", + " target [deg] | data KDUQ WLH CHUQ JLMB | data KDUQ WLH CHUQ JLMB\n", + "--------------------------------------------------------------------------------------------------\n", + " 49Ti 16.6 | 0.52 0.00 0.11 0.25 -0.21 | -0.44 -0.16 -0.07 -0.10 0.11\n", + " 56Fe 15.7 | 0.33 0.03 0.05 0.18 -0.22 | -0.31 -0.20 -0.09 -0.11 0.24\n", + " 64Ni 16.7 | 0.23 0.12 -0.09 0.21 -0.18 | -0.32 -0.18 -0.10 -0.09 0.15\n", + " 70Zn 16.0 | 0.21 0.09 -0.17 0.16 -0.08 | -0.41 -0.23 -0.04 -0.13 0.16\n", + " 90Zr 15.4 | -0.16 -0.11 0.07 -0.04 0.17 | -0.26 -0.14 0.06 -0.06 0.10\n", + " 96Zr 14.4 | -0.20 -0.13 -0.04 -0.11 0.16 | -0.29 -0.10 0.10 -0.01 0.04\n", + " 117Sn 15.0 | -0.38 -0.15 0.01 -0.23 0.09 | -0.08 -0.04 0.08 0.04 -0.00\n", + " 165Ho 16.9 | -0.10 0.01 0.33 0.00 -0.10 | -0.17 0.02 0.20 0.01 -0.02\n", + " 208Pb 17.3 | 0.14 -0.05 0.30 -0.07 0.08 | -- -- -- -- --\n" + ] + } + ], + "source": [ + "# two numbers per target: the analyzing power at the most forward measured angle,\n", + "# whose sign is the feature Gosset et al. tracked across the mass range, and the\n", + "# mean over the measured angles beyond 40 deg, where the data sit on a plateau\n", + "def summarize(theta, curve):\n", + " interpolated = np.interp(theta, angles_deg, curve)\n", + " plateau = interpolated[theta > 40.0]\n", + " # 208Pb has no measured point beyond 40 degrees\n", + " return interpolated[0], plateau.mean() if plateau.size else np.nan\n", + "\n", + "\n", + "models = list(BANDS) + [\"JLMB\"]\n", + "print(f\"{'':>8} {'theta_1':>8} | {'Ay(theta_1)':>34} | {' for theta > 40 deg':>34}\")\n", + "print(\n", + " f\"{'target':>8} {'[deg]':>8} | \"\n", + " + \" \".join(f\"{m:>8}\" for m in [\"data\"] + models)\n", + " + \" | \"\n", + " + \" \".join(f\"{m:>8}\" for m in [\"data\"] + models)\n", + ")\n", + "print(\"-\" * 98)\n", + "for i, d in enumerate(datasets):\n", + " curves = [predictions[name][i][\"Ay_median\"] for name in BANDS] + [jlmb[i].Ay]\n", + " measured_plateau = d[\"Ay\"][d[\"theta\"] > 40.0]\n", + " forward = [d[\"Ay\"][0]] + [summarize(d[\"theta\"], c)[0] for c in curves]\n", + " if measured_plateau.size:\n", + " plateau = [measured_plateau.mean()] + [\n", + " summarize(d[\"theta\"], c)[1] for c in curves\n", + " ]\n", + " plateau = \" \".join(f\"{value:>8.2f}\" for value in plateau)\n", + " else:\n", + " plateau = \" \".join(f\"{'--':>8}\" for _ in range(len(curves) + 1))\n", + " print(\n", + " f\"{d['name']:>8} {d['theta'][0]:>8.1f} | \"\n", + " + \" \".join(f\"{value:>8.2f}\" for value in forward)\n", + " + \" | \"\n", + " + plateau\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "b55768a6", + "metadata": {}, + "source": [ + "Compare with Figs. 4, 6 and 8 of the paper, and with the table above.\n", + "\n", + "The feature Gosset *et al.* tracked across the mass range is the sign of the forward extremum,\n", + "sampled in the table at the most forward measured angle, 15-17$^\\circ$: positive for the\n", + "medium-mass targets and negative for the heavier ones. In the data it runs\n", + "$+0.52, +0.33, +0.23, +0.21$ for $^{49}$Ti, $^{56}$Fe, $^{64}$Ni, $^{70}$Zn and then\n", + "$-0.16, -0.20, -0.38$ for $^{90}$Zr, $^{96}$Zr, $^{117}$Sn. CHUQ reproduces all seven signs and\n", + "KDUQ six, with $^{49}$Ti sitting at zero. Both also get the negative plateau beyond 40$^\\circ$,\n", + "KDUQ at roughly half the observed depth and CHUQ at about a quarter of it. That is the same\n", + "failure mode the paper reported for its own best DWBA calculations: right phase, too small an\n", + "amplitude. WLH does not reproduce the changeover at all, and its band is several times wider than\n", + "the other two throughout.\n", + "\n", + "Two panels should not be read as tests of the model, and the paper says as much: $^{165}$Ho is\n", + "strongly deformed, so a spherical Lane model is the wrong tool, and $^{208}$Pb has four points with\n", + "an exit neutron energy of 3.7 MeV, where the compound-nucleus contribution cannot be neglected.\n", + "They are also the two targets whose forward extremum KDUQ and CHUQ fail to reproduce — flat against\n", + "$-0.10$ for $^{165}$Ho, and the wrong sign for $^{208}$Pb.\n", + "\n", + "The JLMB curve is the interesting outlier: its analyzing power comes out with the opposite sign to\n", + "both phenomenological potentials and to the data, over most of the angular range and for most\n", + "targets. Since $A_y$ here is driven almost entirely by the isovector spin-orbit form factor, and\n", + "JLMB is the one potential whose spin-orbit is a Scheerbaum form factor rather than a fitted\n", + "Woods-Saxon derivative, this says something about the isovector content of that form factor rather\n", + "than about the coupled-channels solution. It is recorded here as an observation, not a result.\n", + "\n", + "The same S-matrices give the cross section, which — as the paper emphasized — is far less\n", + "discriminating. There are no cross-section data in this EXFOR entry; the paper compared against a\n", + "separate Boulder measurement." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "14aaebd4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:02:44.444485Z", + "iopub.status.busy": "2026-09-24T20:02:44.444315Z", + "iopub.status.idle": "2026-09-24T20:02:45.863858Z", + "shell.execute_reply": "2026-09-24T20:02:45.863257Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = grid_figure(r\"$d\\sigma/d\\Omega$ [mb/sr]\")\n", + "for i, (ax, d, curve) in enumerate(zip(axes.flat, datasets, jlmb, strict=True)):\n", + " for name, color in BANDS.items():\n", + " lo, hi = predictions[name][i][\"dsdo\"]\n", + " ax.fill_between(angles_deg, lo, hi, color=color, alpha=0.35, lw=0, label=name)\n", + " ax.plot(angles_deg, curve.dsdo, color=JLMB_INK, lw=1.2, ls=\"--\", label=\"JLMB\")\n", + " ax.set_yscale(\"log\")\n", + " ax.set_ylim(1e-3, 3e1)\n", + " ax.text(0.04, 0.06, d[\"label\"], transform=ax.transAxes, fontsize=13)\n", + "\n", + "axes[0, 0].legend(loc=\"upper right\", fontsize=8, frameon=False)\n", + "fig.suptitle(r\"$(\\vec{p},n)$ cross section to the IAS at $E_p = 22.8$ MeV\", y=0.92)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3b0da5fc", + "metadata": {}, + "source": [ + "## How much does the exact coupling matter?\n", + "\n", + "The first-order approximation to the off-diagonal S-matrix element is the DWBA of\n", + "`jitr.xs.quasielastic_pn`. Feeding both workspaces the same potentials isolates the effect of\n", + "solving the coupled system exactly rather than to first order in $U_1$." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c56a7bdb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-24T20:02:45.865272Z", + "iopub.status.busy": "2026-09-24T20:02:45.865125Z", + "iopub.status.idle": "2026-09-24T20:02:47.613358Z", + "shell.execute_reply": "2026-09-24T20:02:47.612705Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max |Ay(CC) - Ay(DWBA)| = 0.0162\n", + "max relative cross-section difference = 0.0266\n" + ] + } + ], + "source": [ + "d = reference\n", + "workspace_dwba = quasielastic_pn.Workspace(\n", + " d[\"reaction\"],\n", + " d[\"kinematics_entrance\"],\n", + " d[\"kinematics_exit\"],\n", + " jitr.rmatrix.Solver(d[\"nbasis\"]),\n", + " ANGLES,\n", + " LMAX,\n", + " d[\"channel_radius_fm\"],\n", + " tmatrix_abs_tol=0.0,\n", + ")\n", + "rgrid = d[\"workspace\"].radial_grid()\n", + "U_p, U_p_so, U_coulomb = omp_p(\n", + " rgrid, d[\"reaction\"], d[\"kinematics_entrance\"], *params_p\n", + ")\n", + "U_n, U_n_so, _ = omp_n(rgrid, d[\"exit_reaction\"], d[\"kinematics_exit\"], *params_n)\n", + "\n", + "cc = d[\"workspace\"].observables(U_coulomb, U_p, U_p_so, U_n, U_n_so)\n", + "dwba = workspace_dwba.observables(U_coulomb, U_p, U_p_so, U_n, U_n_so)\n", + "\n", + "fig, (ax_ay, ax_xs) = plt.subplots(\n", + " 2, 1, figsize=(7, 7), sharex=True, height_ratios=[1, 1]\n", + ")\n", + "fig.subplots_adjust(hspace=0.08)\n", + "for ax, cc_y, dwba_y in [(ax_ay, cc.Ay, dwba.Ay), (ax_xs, cc.dsdo, dwba.dsdo)]:\n", + " ax.plot(angles_deg, cc_y, color=\"#0072B2\", lw=1.6, label=\"coupled channels\")\n", + " ax.plot(angles_deg, dwba_y, color=JLMB_INK, lw=1.3, ls=\"--\", label=\"DWBA\")\n", + " ax.tick_params(direction=\"in\", top=True, right=True)\n", + "ax_ay.errorbar(\n", + " d[\"theta\"],\n", + " d[\"Ay\"],\n", + " yerr=d[\"Ay_err\"],\n", + " fmt=\"o\",\n", + " ms=3.5,\n", + " color=\"k\",\n", + " lw=0.9,\n", + " capsize=1.5,\n", + " label=\"Gosset (1976)\",\n", + ")\n", + "ax_ay.axhline(0.0, color=\"0.75\", lw=0.7, zorder=0)\n", + "ax_ay.set_ylabel(r\"$A_y$\")\n", + "ax_ay.set_ylim(-1.05, 1.05)\n", + "ax_ay.legend(loc=\"lower left\", fontsize=9, frameon=False)\n", + "ax_xs.set_yscale(\"log\")\n", + "ax_xs.set_ylabel(r\"$d\\sigma/d\\Omega$ [mb/sr]\")\n", + "ax_xs.set_xlabel(r\"$\\theta_{\\rm c.m.}$ [deg]\")\n", + "ax_xs.set_xlim(0, 140)\n", + "fig.suptitle(rf\"{d['label']}$(\\vec{{p}},n)$ to the IAS, KD03 parameters\", y=0.92)\n", + "plt.show()\n", + "\n", + "print(f\"max |Ay(CC) - Ay(DWBA)| = {np.max(np.absolute(cc.Ay - dwba.Ay)):.4f}\")\n", + "print(\n", + " f\"max relative cross-section difference = {np.max(np.absolute(cc.dsdo / dwba.dsdo - 1)):.4f}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "588d38e0", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "Propagating three uncertainty-quantified global optical potentials through an exact\n", + "coupled-channels Lane calculation reproduces the qualitative structure of the Gosset *et al.*\n", + "analyzing powers without any adjustment to these data. For KDUQ and CHUQ the sign of the forward\n", + "extremum and its mass dependence come out right, and so does the sign of the mid-angle plateau —\n", + "but at about half the measured depth, and the posterior bands nowhere near cover the data.\n", + "\n", + "That gap is the point of the original paper. The analyzing power of a quasi-elastic $(p,n)$\n", + "reaction depends on the isovector spin-orbit part of the Lane potential, which global elastic\n", + "analyses barely constrain. Here it is not a fitted quantity at all: it is the difference between\n", + "two spin-orbit potentials that were each calibrated to elastic scattering, so its uncertainty is\n", + "not represented in the bands above, and the spread between KDUQ, CHUQ, WLH and JLMB is a much\n", + "better picture of how poorly it is known than any one band is. Gosset *et al.* needed\n", + "$V^{(1)}_{so} \\approx 4$-6 MeV to fit these data, against the $\\approx 2$ MeV a simple\n", + "free-nucleon-force argument predicts. The measurements remain a good candidate for calibrating\n", + "that term directly." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/jitr/xs/lane_pn.py b/src/jitr/xs/lane_pn.py index 1e1a2abd..235d9cc0 100644 --- a/src/jitr/xs/lane_pn.py +++ b/src/jitr/xs/lane_pn.py @@ -28,7 +28,9 @@ from ..utils.kinematics import ChannelKinematics from .elastic import check_angles from .quasielastic_pn import ( + QuasielasticPnXS, isovector_factor, + pn_observables, pn_potentials, spin_half_transition_geometry, ) @@ -246,6 +248,36 @@ def xs( ) return self.xs_from_smatrix(S) + def observables( + self, + U_p_coulomb: npt.ArrayLike, + U_p_central: npt.ArrayLike, + U_p_spin_orbit: npt.ArrayLike | None = None, + U_n_central: npt.ArrayLike | None = None, + U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, + ) -> QuasielasticPnXS: + """ + Differential cross section, analyzing power and spin-rotation function + in the outgoing neutron angle, from the coupled-channels S-matrix. + + Args are as for :meth:`rsmatrix`. + + Returns: + Observables at ``self.angles``; the cross section is in mb/sr. + """ + _, S = self.rsmatrix( + U_p_coulomb, + U_p_central, + U_p_spin_orbit, + U_n_central, + U_n_spin_orbit, + U1_central, + U1_spin_orbit, + ) + return self.observables_from_smatrix(S) + def integrated_xs( self, U_p_coulomb: npt.ArrayLike, @@ -293,8 +325,43 @@ def xs_from_smatrix(self, S: ComplexArray) -> FloatArray: Returns: Differential cross section at ``self.angles`` in mb/sr. """ - f = np.einsum("abljt,lj->abt", self.geometric_factor, S[:, :, NEUTRON, PROTON]) - return 10 * 0.5 * np.sum(np.abs(f) ** 2, axis=(0, 1)) + return self.observables_from_smatrix(S).dsdo + + def amplitudes_from_smatrix(self, S: ComplexArray) -> ComplexArray: + r""" + Spin-1/2 transition amplitude matrix from the coupled S-matrix. + + .. math:: + f_{m m'}(\theta) = \frac{\sqrt{4\pi}}{2 i k_p} \sum_{lj} + \sqrt{2l+1}\langle l 0 \tfrac{1}{2} m | j m \rangle + \langle l, m-m'; \tfrac{1}{2} m' | j m \rangle + e^{i(\sigma_l^p + \sigma_l^n)} S^{lj}_{np} Y_l^{m-m'}(\theta, 0) + + Args: + S: Flux-normalized S-matrix from :meth:`rsmatrix`. + + Returns: + Amplitudes :math:`f_{mm'}(\theta)` with shape + ``(2, 2, len(self.angles))``. + """ + return np.einsum( + "abljt,lj->abt", self.geometric_factor, S[:, :, NEUTRON, PROTON] + ) + + def observables_from_smatrix(self, S: ComplexArray) -> QuasielasticPnXS: + r""" + Observables from the coupled S-matrix, + :math:`\frac{d\sigma}{d\Omega} = \frac{1}{2}\sum_{m m'} |f_{m m'}|^2` + and the analyzing power and spin-rotation function of + :func:`jitr.xs.quasielastic_pn.pn_observables`. + + Args: + S: Flux-normalized S-matrix from :meth:`rsmatrix`. + + Returns: + Observables at ``self.angles``; the cross section is in mb/sr. + """ + return pn_observables(self.amplitudes_from_smatrix(S)) def integrated_xs_from_smatrix(self, S: ComplexArray) -> float: r""" diff --git a/src/jitr/xs/quasielastic_pn.py b/src/jitr/xs/quasielastic_pn.py index 8555eef5..7d891031 100644 --- a/src/jitr/xs/quasielastic_pn.py +++ b/src/jitr/xs/quasielastic_pn.py @@ -1,5 +1,7 @@ """DWBA workspaces for quasi-elastic ``(p,n)`` scattering observables.""" +from dataclasses import dataclass + import numpy as np import numpy.typing as npt from scipy.special import gamma, sph_harm_y @@ -15,6 +17,21 @@ FloatArray = npt.NDArray[np.float64] +@dataclass +class QuasielasticPnXS: + """Container for quasi-elastic ``(p,n)`` observables. + + Attributes: + dsdo: Differential cross section in mb/sr. + Ay: Analyzing power. + Q: Spin-rotation function. + """ + + dsdo: FloatArray + Ay: FloatArray + Q: FloatArray + + def isovector_factor(reaction: Reaction) -> float: r"""Return :math:`\sqrt{|N-Z|}/(N-Z-1)` for the target of ``reaction``. @@ -151,6 +168,59 @@ def spin_half_transition_geometry(lmax: int, angles: FloatArray) -> ComplexArray return geometry +def pn_observables( + f: ComplexArray, xs_factor: float = 0.5, eps: float = 1e-30 +) -> QuasielasticPnXS: + r"""Observables from the spin-1/2 transition amplitude matrix. + + The scattering plane is taken at :math:`\phi = 0`, so the normal is + :math:`\hat n = \hat k_{in} \times \hat k_{out} = \hat y` and, for a + transition with :math:`l = s = j = 0` transfer, the amplitude matrix is + + .. math:: + M = A + B\, \sigma \cdot \hat n , + + with :math:`A` the non-spin-flip and :math:`B` the spin-flip amplitude. + In the ``f[m, m']`` basis of :func:`spin_half_transition_geometry` this is + :math:`A = f[1, 1] = f[0, 0]` and + :math:`\langle -|M|+\rangle = f[1, 0] = i B`, which gives + + .. math:: + \frac{d\sigma}{d\Omega} = \frac{1}{2}\sum_{mm'}|f_{mm'}|^2 + = |A|^2 + |B|^2, \qquad + A_y = \frac{2\,\mathrm{Im}(A^* f[1,0])}{|A|^2 + |B|^2}, \qquad + Q = \frac{2\,\mathrm{Re}(A^* f[1,0])}{|A|^2 + |B|^2}. + + This is the same convention as :func:`jitr.xs.elastic.differential_elastic_xs` + and as Eq. (12) of Gosset, Mayer and Escudie, Phys. Rev. C 14, 878 (1976). + + The cross section is the full sum over ``m, m'``. The analyzing power and + spin-rotation function, on the other hand, are only meaningful when ``f`` + really does reduce to two amplitudes, i.e. when the transition conserves + ``l`` and ``j`` on a spin-0 target so that ``f[0, 0] == f[1, 1]`` and + ``f[0, 1] == -f[1, 0]``. + + Args: + f: Amplitude matrix with shape ``(2, 2, len(angles))`` indexed by + ``[m, m', theta]``, with ``m, m'`` in ``(-1/2, +1/2)``. + xs_factor: Overall factor multiplying :math:`\sum_{mm'}|f_{mm'}|^2` to + give the cross section in fm^2/sr. Defaults to the ``1/(2s+1)`` + spin average of a flux-normalized S-matrix amplitude. + eps: Floor on the cross section used to regularize the ratios. + + Returns: + The differential cross section in mb/sr, the analyzing power and the + spin-rotation function at each angle. + """ + total = np.sum(np.absolute(f) ** 2, axis=(0, 1)) + denom = np.maximum(0.5 * total, eps) + return QuasielasticPnXS( + dsdo=10.0 * xs_factor * total, + Ay=2.0 * np.imag(np.conjugate(f[1, 1]) * f[1, 0]) / denom, + Q=2.0 * np.real(np.conjugate(f[1, 1]) * f[1, 0]) / denom, + ) + + class System: r""" System for (p,n) quasi-elastic scattering observables for local interactions @@ -520,8 +590,36 @@ def xs( Returns: Differential cross section for the (p,n) reaction in mb/Sr. """ + return self.observables( + U_p_coulomb=U_p_coulomb, + U_p_central=U_p_central, + U_p_spin_orbit=U_p_spin_orbit, + U_n_central=U_n_central, + U_n_spin_orbit=U_n_spin_orbit, + U1_central=U1_central, + U1_spin_orbit=U1_spin_orbit, + ).dsdo - Tlj, Sn, Sp = self.tmatrix( + def observables( + self, + U_p_coulomb: npt.ArrayLike, + U_p_central: npt.ArrayLike, + U_p_spin_orbit: npt.ArrayLike | None = None, + U_n_central: npt.ArrayLike | None = None, + U_n_spin_orbit: npt.ArrayLike | None = None, + U1_central: npt.ArrayLike | None = None, + U1_spin_orbit: npt.ArrayLike | None = None, + ) -> QuasielasticPnXS: + """ + Differential cross section, analyzing power and spin-rotation function + for (p,n) quasi-elastic scattering in DWBA. + + Args are as for :meth:`xs`. + + Returns: + Observables at ``self.angles``; the cross section is in mb/Sr. + """ + Tlj, _, _ = self.tmatrix( U_p_coulomb=U_p_coulomb, U_p_central=U_p_central, U_p_spin_orbit=U_p_spin_orbit, @@ -530,7 +628,34 @@ def xs( U1_central=U1_central, U1_spin_orbit=U1_spin_orbit, ) + return self.observables_from_tmatrix(Tlj) + + def amplitudes_from_tmatrix(self, Tlj: ComplexArray) -> ComplexArray: + r""" + Spin-1/2 transition amplitude matrix from the DWBA T-matrix. + + Args: + Tlj: Partial-wave T-matrix from :meth:`tmatrix`, with shape + ``(lmax + 1, 2)`` indexed by ``[l, j]``. + + Returns: + Amplitudes :math:`T_{mm'}(\theta)` with shape + ``(2, 2, len(self.angles))``. + """ # geometric_factor is zero wherever the (l, j, m, m') combination is # not allowed, so the sum needs no further selection rules - Tmmp = np.einsum("abljt,lj->abt", self.geometric_factor, Tlj) - return self.xs_factor * 10 * np.sum(np.absolute(Tmmp) ** 2, axis=(0, 1)) + return np.einsum("abljt,lj->abt", self.geometric_factor, Tlj) + + def observables_from_tmatrix(self, Tlj: ComplexArray) -> QuasielasticPnXS: + """ + Observables from the DWBA T-matrix. + + Args: + Tlj: Partial-wave T-matrix from :meth:`tmatrix`. + + Returns: + Observables at ``self.angles``; the cross section is in mb/Sr. + """ + return pn_observables( + self.amplitudes_from_tmatrix(Tlj), xs_factor=self.xs_factor + ) diff --git a/tests/test_pn_analyzing_power.py b/tests/test_pn_analyzing_power.py new file mode 100644 index 00000000..141bdd42 --- /dev/null +++ b/tests/test_pn_analyzing_power.py @@ -0,0 +1,218 @@ +"""Polarization observables for quasi-elastic (p,n) scattering to the IAS. + +The analyzing power of a quasi-elastic (p,n) reaction is the observable that +Gosset, Mayer and Escudie, Phys. Rev. C 14, 878 (1976) used to constrain the +isovector spin-orbit part of the Lane potential. These tests pin the amplitude +decomposition and the sign convention it is built on. +""" + +import numpy as np +import pytest +from scipy.special import eval_legendre, lpmv + +from jitr.optical_potentials.potential_forms import ( + coulomb_charged_sphere, + woods_saxon_safe, +) +from jitr.reactions import Reaction +from jitr.rmatrix import Solver +from jitr.xs import lane_pn, quasielastic_pn + +ANGLES = np.linspace(1e-3, np.pi - 1e-3, 721) +LMAX = 15 +RADIUS = 14.0 + + +def _thomas(r, depth, R, a): + x = np.exp((r - R) / a) + return -depth * x / (1 + x) ** 2 / (a * r) + + +@pytest.fixture(scope="module") +def setup(): + reaction = Reaction((48, 20), (1, 1), (1, 0), (48, 21)) + ke = reaction.kinematics(35.0, relativistic=False) + kx = reaction.kinematics_exit(ke, 6.67, relativistic=False) + solver = Solver(35) + cc = lane_pn.Workspace(reaction, ke, kx, solver, ANGLES, LMAX, RADIUS) + dwba = quasielastic_pn.Workspace( + reaction, ke, kx, solver, ANGLES, LMAX, RADIUS, tmatrix_abs_tol=0 + ) + return cc, dwba + + +def _potentials(cc, spin_orbit=True): + r = cc.radial_grid() + so = 1.0 if spin_orbit else 0.0 + return { + "U_p_coulomb": coulomb_charged_sphere(r, 20, 4.7), + "U_p_central": (-50.0 - 8.0j) * woods_saxon_safe(r, 4.4, 0.65), + "U_p_spin_orbit": so * _thomas(r, 6.0, 4.0, 0.6), + "U_n_central": (-46.0 - 8.0j) * woods_saxon_safe(r, 4.4, 0.65), + "U_n_spin_orbit": so * _thomas(r, 5.5, 4.0, 0.6), + } + + +def _default_U1(ws, p): + f = ws.isovector_factor + return ( + -(p["U_n_central"] - p["U_p_central"]) * f, + -(p["U_n_spin_orbit"] - p["U_p_spin_orbit"]) * f, + ) + + +def _legendre_amplitudes(prefactor, partial_waves): + """Rebuild the non-spin-flip and spin-flip amplitudes from Legendre sums. + + This is Eq. (13) of Gosset et al., an implementation independent of the + Clebsch-Gordan / spherical-harmonic construction in + ``spin_half_transition_geometry``:: + + X = sum_l c_l [(l + 1) I_l+ + l I_l-] P_l(cos t) + Y = sum_l c_l [I_l+ - I_l-] P^1_l(cos t) + + Args: + prefactor: ``c_l`` including the ``1/sqrt(4 pi)`` that the m-basis + geometry carries, with shape ``(lmax + 1,)``. + partial_waves: ``I_lj`` with shape ``(lmax + 1, 2)``, ``j`` indexing + ``(l + 1/2, l - 1/2)``. + """ + ls = np.arange(partial_waves.shape[0])[:, np.newaxis] + costheta = np.cos(ANGLES)[np.newaxis, :] + plus = partial_waves[:, 0][:, np.newaxis] + minus = partial_waves[:, 1][:, np.newaxis] + c = prefactor[:, np.newaxis] + X = np.sum(c * ((ls + 1) * plus + ls * minus) * eval_legendre(ls, costheta), axis=0) + Y = np.sum(c * (plus - minus) * lpmv(1, ls, costheta), axis=0) + return X, Y + + +def test_dsdo_is_the_summed_amplitude_matrix(setup): + """The cross section is 1/(2s+1) times the full sum over m, m'. + + ``xs`` and ``xs_from_smatrix`` both route through ``pn_observables`` now, so + comparing them to it would be tautological; the sum is written out here + instead. + """ + cc, dwba = setup + p = _potentials(cc) + + _, S = cc.rsmatrix(**p) + f = cc.amplitudes_from_smatrix(S) + np.testing.assert_allclose( + cc.observables_from_smatrix(S).dsdo, + 10.0 * 0.5 * np.sum(np.absolute(f) ** 2, axis=(0, 1)), + rtol=1e-14, + ) + + Tlj, _, _ = dwba.tmatrix(**p) + t = dwba.amplitudes_from_tmatrix(Tlj) + np.testing.assert_allclose( + dwba.observables_from_tmatrix(Tlj).dsdo, + dwba.xs_factor * 10.0 * np.sum(np.absolute(t) ** 2, axis=(0, 1)), + rtol=1e-14, + ) + + +def test_amplitude_symmetries(setup): + """A spin-1/2 transition on a spin-0 target has only two amplitudes.""" + cc, dwba = setup + p = _potentials(cc) + _, S = cc.rsmatrix(**p) + f = cc.amplitudes_from_smatrix(S) + atol = 1e-14 * np.max(np.absolute(f)) + np.testing.assert_allclose(f[0, 0], f[1, 1], rtol=0, atol=atol) + np.testing.assert_allclose(f[0, 1], -f[1, 0], rtol=0, atol=atol) + + +@pytest.mark.parametrize("workspace", ["cc", "dwba"]) +def test_ay_vanishes_without_spin_orbit(setup, workspace): + """With no spin-orbit anywhere the transition cannot analyze the beam. + + This is the statement below Eq. (3e) of Gosset et al.: with the entrance + and exit spin-orbit potentials zero, the isovector spin-orbit form factor + vanishes too and the radial matrix elements stop depending on j, so the + spin-flip amplitude is identically zero. + """ + cc, dwba = setup + p = _potentials(cc, spin_orbit=False) + zero = np.zeros_like(cc.radial_grid(), dtype=np.complex128) + + if workspace == "cc": + obs = cc.observables(**p, U1_spin_orbit=zero) + _, S = cc.rsmatrix(**p, U1_spin_orbit=zero) + f = cc.amplitudes_from_smatrix(S) + else: + obs = dwba.observables(**p, U1_spin_orbit=zero) + Tlj, _, _ = dwba.tmatrix(**p, U1_spin_orbit=zero) + f = dwba.amplitudes_from_tmatrix(Tlj) + + non_flip, spin_flip = f[1, 1], f[1, 0] + assert np.max(np.absolute(spin_flip)) < 1e-12 * np.max(np.absolute(non_flip)) + assert np.max(np.absolute(obs.Ay)) < 1e-12 + assert np.max(np.absolute(obs.Q)) < 1e-12 + + +def test_ay_nonzero_with_spin_orbit(setup): + """The converse: equal but non-zero p/n spin-orbits still analyze. + + The isovector spin-orbit form factor vanishes here, but the distorted + waves are still j-dependent, so Ay does not. + """ + cc, _ = setup + p = _potentials(cc) + p["U_n_spin_orbit"] = p["U_p_spin_orbit"] + zero = np.zeros_like(cc.radial_grid(), dtype=np.complex128) + assert np.max(np.absolute(cc.observables(**p, U1_spin_orbit=zero).Ay)) > 0.1 + + +def test_amplitudes_match_legendre_partial_wave_sum(setup): + """Pin the geometry, and the sign of Ay, against an independent sum. + + ``scipy.special.lpmv`` carries the Condon-Shortley phase, which is the + same convention ``jitr.xs.elastic`` uses for its spin-flip amplitude, so + this also pins the (p,n) analyzing power to the elastic one. + """ + cc, dwba = setup + p = _potentials(cc) + norm = 1.0 / np.sqrt(4 * np.pi) + + _, S = cc.rsmatrix(**p) + f = cc.amplitudes_from_smatrix(S) + prefactor = ( + norm + * np.sqrt(4 * np.pi) + / (2j * cc.kinematics_entrance.k) + * np.exp(1j * cc.sigma_c) + ) + X, Y = _legendre_amplitudes(prefactor, S[:, :, lane_pn.NEUTRON, lane_pn.PROTON]) + scale = np.max(np.absolute(X)) + np.testing.assert_allclose(f[1, 1], X, rtol=0, atol=1e-12 * scale) + np.testing.assert_allclose(f[1, 0], Y, rtol=0, atol=1e-12 * scale) + + Tlj, _, _ = dwba.tmatrix(**p) + t = dwba.amplitudes_from_tmatrix(Tlj) + prefactor = ( + norm + * (4 * np.pi) ** 1.5 + / (dwba.kinematics_entrance.k * dwba.kinematics_exit.k) + * np.exp(1j * dwba.sigma_c) + ) + X, Y = _legendre_amplitudes(prefactor, Tlj) + scale = np.max(np.absolute(X)) + np.testing.assert_allclose(t[1, 1], X, rtol=0, atol=1e-12 * scale) + np.testing.assert_allclose(t[1, 0], Y, rtol=0, atol=1e-12 * scale) + + +def test_weak_coupling_ay_matches_dwba(setup): + """As U1 -> 0 the coupled-channels analyzing power reduces to DWBA.""" + cc, dwba = setup + p = _potentials(cc) + U1_central, U1_spin_orbit = _default_U1(cc, p) + eps = 1e-3 + weak = dict(U1_central=eps * U1_central, U1_spin_orbit=eps * U1_spin_orbit) + + # Ay is a ratio of amplitudes, both linear in U1, so it needs no rescaling + np.testing.assert_allclose( + cc.observables(**p, **weak).Ay, dwba.observables(**p, **weak).Ay, atol=1e-4 + ) From 10d424293e4988b40e212adc15fde141d51bf89b Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 24 Sep 2026 19:49:24 -0400 Subject: [PATCH 09/11] fix single-channel solve in coupled DWBA notebook passing two incoming weights --- examples/notebooks/test_coupled_single_dwba.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/notebooks/test_coupled_single_dwba.ipynb b/examples/notebooks/test_coupled_single_dwba.ipynb index 6a8a5c36..98a41f59 100644 --- a/examples/notebooks/test_coupled_single_dwba.ipynb +++ b/examples/notebooks/test_coupled_single_dwba.ipynb @@ -304,7 +304,6 @@ " solver.radial_grid(channels_uncoupled[0].a, channels_uncoupled[0].k[0]),\n", " *params_scalar,\n", " ),\n", - " weights=np.array([1, 1]),\n", ")\n", "R2, S2, u2 = solver.solve(\n", " channels_uncoupled[1],\n", From 7f684eadf1247ef217d1895e157fa97b06d1c160 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 24 Sep 2026 21:05:11 -0400 Subject: [PATCH 10/11] fix new nb and update to exfor_tools v1.3 --- examples/notebooks/qepn_analyzing_power.ipynb | 630 ++++-------------- pyproject.toml | 2 +- uv.lock | 11 +- 3 files changed, 119 insertions(+), 524 deletions(-) diff --git a/examples/notebooks/qepn_analyzing_power.ipynb b/examples/notebooks/qepn_analyzing_power.ipynb index 015efd8a..c501d509 100644 --- a/examples/notebooks/qepn_analyzing_power.ipynb +++ b/examples/notebooks/qepn_analyzing_power.ipynb @@ -14,14 +14,7 @@ "cell_type": "code", "execution_count": 1, "id": "135b5879", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:22.824184Z", - "iopub.status.busy": "2026-09-24T20:01:22.824049Z", - "iopub.status.idle": "2026-09-24T20:01:22.831715Z", - "shell.execute_reply": "2026-09-24T20:01:22.830825Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Google Colab setup: installs jitr and this notebook's extra dependencies.\n", @@ -70,10 +63,7 @@ "$$ A_y = \\frac{2\\,\\mathrm{Im}(Y X^*)}{|X|^2 + |Y|^2}. $$\n", "\n", "The spin-flip amplitude $Y$ is built from the *difference* between the $j = l + \\tfrac{1}{2}$ and\n", - "$j = l - \\tfrac{1}{2}$ radial matrix elements. If the entrance and exit distorting spin-orbit\n", - "potentials both vanish, those matrix elements stop depending on $j$, $Y$ vanishes, and\n", - "$A_y \\equiv 0$ identically. So $A_y$ measures spin-orbit physics and almost nothing else — which\n", - "is why Gosset *et al.* used it to try to pin down the isovector spin-orbit depth $V^{(1)}_{so}$.\n", + "$j = l - \\tfrac{1}{2}$ radial matrix elements. \n", "\n", "This notebook solves the two coupled Lane channels exactly with\n", "[`jitr.xs.lane_pn`](https://jitr.readthedocs.io/en/latest/autoapi/jitr/xs/lane_pn/index.html)\n", @@ -81,23 +71,14 @@ "potentials — KDUQ, WLH and CHUQ — plus a deterministic JLMB calculation, through to $A_y$ for all\n", "nine targets.\n", "\n", - "A note on the targets: $^{49}$Ti, $^{117}$Sn and $^{165}$Ho have non-zero ground-state spin, but the\n", - "transition transfers no angular momentum, so the target spin is a spectator and the spin-1/2\n", - "geometry used here still applies." + "Notw that, while $^{49}$Ti, $^{117}$Sn and $^{165}$Ho all have non-zero ground-state spin, the measured transitions transfer no angular momentum, so the target spin is a spectator and the spin-1/2 geometry used here is still reasonable." ] }, { "cell_type": "code", "execution_count": 2, "id": "d732cdc9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:22.833074Z", - "iopub.status.busy": "2026-09-24T20:01:22.832914Z", - "iopub.status.idle": "2026-09-24T20:01:24.631476Z", - "shell.execute_reply": "2026-09-24T20:01:24.630646Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -133,32 +114,35 @@ "source": [ "## Pulling the measurements from EXFOR\n", "\n", - "Each of the nine targets is a subentry of O0669, tabulated as an analyzing power against the\n", - "center-of-mass angle, together with the excitation energy of the analog state in the residual\n", - "nucleus. That excitation energy is what fixes the outgoing neutron energy, so we read it from the\n", - "entry rather than hard-coding it." + "Each of the nine targets is a subentry of O0669, tabulated as an analyzing power against the center-of-mass angle, together with the excitation energy of the analog state in the residual nucleus. That excitation energy is what fixes the outgoing neutron energy, so we read it from the entry to set the kinematics in the exit channels." ] }, { "cell_type": "code", "execution_count": 3, - "id": "8d1eb7d8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:24.633068Z", - "iopub.status.busy": "2026-09-24T20:01:24.632870Z", - "iopub.status.idle": "2026-09-24T20:01:25.618763Z", - "shell.execute_reply": "2026-09-24T20:01:25.618072Z" - } - }, + "id": "9de4a4b9-c983-4d82-89ca-4a1f1f9d9b23", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" + "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n", + "1.3\n" ] - }, + } + ], + "source": [ + "import exfor_tools\n", + "print(exfor_tools.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d1eb7d8", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", @@ -177,17 +161,7 @@ ], "source": [ "from exfor_tools import ExforEntry\n", - "from exfor_tools.parsing import quantity_matches, quantity_symbols\n", "from exfor_tools.reaction import Reaction as ExforReaction\n", - "\n", - "# exfor-tools <= 1.2 maps \"Ay\" only to [\"POL/DA\", \"ANA\"], but EXFOR qualifies a measurement\n", - "# resolved to a particular level of the residual with \"PAR\" -- as it must for a transition to\n", - "# the analog state. Fixed upstream, so this is a no-op against a newer exfor-tools.\n", - "par_ay = [\"PAR\", \"POL/DA\", \"ANA\"]\n", - "if par_ay not in quantity_matches[\"Ay\"]:\n", - " quantity_matches[\"Ay\"].append(par_ay)\n", - " quantity_symbols[tuple(par_ay)] = r\"$A_y$\"\n", - "\n", "# the nine targets of Table I, in the order the paper presents them\n", "TARGETS = [\n", " (49, 22),\n", @@ -238,16 +212,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "1d3f5bd4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:25.620297Z", - "iopub.status.busy": "2026-09-24T20:01:25.620045Z", - "iopub.status.idle": "2026-09-24T20:01:25.624263Z", - "shell.execute_reply": "2026-09-24T20:01:25.623468Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -289,35 +256,21 @@ "id": "8b46aad9", "metadata": {}, "source": [ - "## Building one coupled-channels workspace per target\n", + "## Compiling one coupled-channels workspace per target\n", "\n", "`jitr.xs.lane_pn.Workspace` solves the two-channel problem\n", "\n", "$$ \\left[T_l + U_{pp} - E_p\\right] u_p + U_1 u_n = 0, \\qquad\n", " \\left[T_l + U_{nn} - E_n\\right] u_n + U_1 u_p = 0 $$\n", "\n", - "for every $(l, j)$, with an incoming wave in the proton channel only. The two channels have\n", - "different wavenumbers, reduced masses and Sommerfeld parameters but share one physical channel\n", - "radius, and the R-matrix solution gives the full $2 \\times 2$ S-matrix, whose off-diagonal element\n", - "is the charge-exchange amplitude.\n", - "\n", - "The exit channel needs its own `Reaction` so that the neutron optical potential is evaluated on the\n", - "*residual* nucleus rather than on the target. Building the solvers and workspaces in their own cell\n", - "keeps the numba compilation and the Lagrange-mesh precompute out of the sample loop below." + "for every $(l, j)$, with an incoming wave in the proton channel only." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "026ba475", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:25.625634Z", - "iopub.status.busy": "2026-09-24T20:01:25.625490Z", - "iopub.status.idle": "2026-09-24T20:01:27.109730Z", - "shell.execute_reply": "2026-09-24T20:01:27.108973Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -384,100 +337,6 @@ " )" ] }, - { - "cell_type": "markdown", - "id": "44521e19", - "metadata": {}, - "source": [ - "## Checking convergence\n", - "\n", - "The isovector coupling is weak, so the charge-exchange S-matrix element falls off quickly with $l$\n", - "and the calculation converges at modest $l_{max}$ and basis size. Doubling $l_{max}$ and enlarging\n", - "the channel radius move $A_y$ by less than $4 \\times 10^{-3}$ in absolute terms, on a quantity\n", - "bounded by 1 — though note that near a zero crossing that is not a small *relative* shift, and this\n", - "checks one target rather than all nine. The distorting potentials here are the original frequentist\n", - "Koning-Delaroche parameters; the posterior draws below are no harder to converge." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c1a51695", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:27.111114Z", - "iopub.status.busy": "2026-09-24T20:01:27.110994Z", - "iopub.status.idle": "2026-09-24T20:01:34.220520Z", - "shell.execute_reply": "2026-09-24T20:01:34.219926Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "56Fe, relative to lmax=25, a=10.66 fm, nbasis=20\n", - " lmax= 25 a=10.66 fm nbasis= 30 max |dAy| = 3.77e-03\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " lmax= 40 a=10.66 fm nbasis= 20 max |dAy| = 0.00e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " lmax= 40 a=16.00 fm nbasis= 35 max |dAy| = 3.90e-03\n" - ] - } - ], - "source": [ - "reference = datasets[1] # 56Fe\n", - "omp_p, omp_n = kduq.KDUQ(PROTON), kduq.KDUQ(NEUTRON)\n", - "# the original frequentist Koning-Delaroche set, not a central value of the KDUQ posterior\n", - "params_p = kduq.get_kd03(PROTON)\n", - "params_n = kduq.get_kd03(NEUTRON)\n", - "\n", - "\n", - "def ay_at(lmax, channel_radius_fm, nbasis):\n", - " workspace = lane_pn.Workspace(\n", - " reference[\"reaction\"],\n", - " reference[\"kinematics_entrance\"],\n", - " reference[\"kinematics_exit\"],\n", - " jitr.rmatrix.Solver(nbasis),\n", - " ANGLES,\n", - " lmax,\n", - " channel_radius_fm,\n", - " )\n", - " rgrid = workspace.radial_grid()\n", - " U_p, U_p_so, U_coulomb = omp_p(\n", - " rgrid, reference[\"reaction\"], reference[\"kinematics_entrance\"], *params_p\n", - " )\n", - " U_n, U_n_so, _ = omp_n(\n", - " rgrid, reference[\"exit_reaction\"], reference[\"kinematics_exit\"], *params_n\n", - " )\n", - " return workspace.observables(U_coulomb, U_p, U_p_so, U_n, U_n_so).Ay\n", - "\n", - "\n", - "baseline = ay_at(LMAX, reference[\"channel_radius_fm\"], reference[\"nbasis\"])\n", - "print(\n", - " f\"{reference['name']}, relative to lmax={LMAX}, a={reference['channel_radius_fm']:.2f} fm, nbasis={reference['nbasis']}\"\n", - ")\n", - "for lmax, a_fm, nbasis in [\n", - " (LMAX, reference[\"channel_radius_fm\"], reference[\"nbasis\"] + 10),\n", - " (40, reference[\"channel_radius_fm\"], reference[\"nbasis\"]),\n", - " (40, 16.0, 35),\n", - "]:\n", - " shift = np.max(np.absolute(ay_at(lmax, a_fm, nbasis) - baseline))\n", - " print(\n", - " f\" lmax={lmax:3d} a={a_fm:5.2f} fm nbasis={nbasis:3d} max |dAy| = {shift:.2e}\"\n", - " )" - ] - }, { "cell_type": "markdown", "id": "76ec66a9", @@ -485,67 +344,27 @@ "source": [ "## Propagating the optical model posteriors\n", "\n", - "KDUQ, WLH and CHUQ each ship a posterior sample of their parameter vectors, so a predictive band\n", - "for $A_y$ is one solve per draw. Nothing here is a fit to these data: the potentials were\n", - "calibrated to elastic scattering, and the Lane transition potential is taken to be the default\n", - "isovector difference\n", + "Let's propagate the KDUQ, WLH and CHUQ posteriors through the $(p,n)$\n", + "$A_y$.\n", + "\n", + "Nothing here is a fit to these data: the potentials were calibrated to elastic scattering, and the Lane transition potential is taken to be the isovector difference\n", "\n", - "$$ U_1 = -\\left(U_n - U_p\\right) \\frac{\\sqrt{|N - Z|}}{N - Z - 1}, $$\n", + "$$ U_1(E) = -\\left(U_n(E - Q - E_x) - U_p(E)\\right) \\frac{\\sqrt{|N - Z|}}{N - Z - 1}, $$\n", "\n", - "evaluated separately for the central and spin-orbit terms — so the isovector spin-orbit form factor\n", - "that the analyzing power is sensitive to comes entirely from the difference between the proton and\n", - "neutron spin-orbit potentials of each global parameterization.\n", + "evaluated separately for the central and spin-orbit terms — so the isovector spin-orbit form factor that the analyzing power is sensitive to comes entirely from the difference between the proton and neutron spin-orbit potentials of each global parameterization. Note that the neutron and proton potentials going into $U_1$ are evaluated at the energies of their respective channels.\n", "\n", - "CHUQ deserves a word: CH89 is Lane-consistent by construction, so it is the one built-in whose\n", - "proton and neutron potentials really are the $U_{pT}$ and $U_{nA}$ of the equations above, and\n", - "whose difference therefore really is $U_1$. For KDUQ and WLH the same construction is an\n", - "approximation, and the diagonal potentials may already absorb part of the coupling." + "CHUQ deserves a word: CH89 is Lane-consistent by construction, so it is the one built-in whose proton and neutron potentials really are the $U_{pT}$ and $U_{nA}$ of the equations above, and whose difference therefore really is $U_1$. For KDUQ and WLH the same construction is an approximation, and the diagonal potentials may already absorb part of the coupling." ] }, { "cell_type": "code", "execution_count": 7, "id": "eb337795", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:01:34.226979Z", - "iopub.status.busy": "2026-09-24T20:01:34.226796Z", - "iopub.status.idle": "2026-09-24T20:02:43.145414Z", - "shell.execute_reply": "2026-09-24T20:02:43.144610Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kyle/umich/jitr/src/jitr/optical_potentials/kduq.py:470: RuntimeWarning: overflow encountered in exp\n", - " d2 = d2_0 + d2_A / (1 + np.exp((A - d2_A3) / d2_A2))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "KDUQ: 416 posterior draws x 9 targets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WLH: 1000 posterior draws x 9 targets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHUQ: 208 posterior draws x 9 targets\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ + "from tqdm import tqdm\n", + "\n", "OMPS = {\n", " \"KDUQ\": (\n", " kduq.KDUQ(PROTON),\n", @@ -570,7 +389,7 @@ " n_draws = min(n_draws, 50)\n", "\n", " bands = []\n", - " for d in datasets:\n", + " for d in tqdm(datasets):\n", " workspace = d[\"workspace\"]\n", " rgrid = workspace.radial_grid()\n", " Ay = np.zeros((len(ANGLES), n_draws))\n", @@ -594,9 +413,68 @@ " \"dsdo_median\": np.median(dsdo, axis=1),\n", " }\n", " )\n", - " return n_draws, bands\n", - "\n", - "\n", + " return n_draws, bands" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d2e1cc24-28f7-48f7-9c38-76b704f12b80", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/9 [00:00" ] @@ -754,7 +620,7 @@ " for name, color in BANDS.items():\n", " lo, hi = predictions[name][i][\"Ay\"]\n", " ax.fill_between(angles_deg, lo, hi, color=color, alpha=0.35, lw=0, label=name)\n", - " ax.plot(angles_deg, predictions[name][i][\"Ay_median\"], color=color, lw=1.2)\n", + " #ax.plot(angles_deg, predictions[name][i][\"Ay_median\"], color=color, lw=1.2)\n", " ax.plot(angles_deg, curve.Ay, color=JLMB_INK, lw=1.2, ls=\"--\", label=\"JLMB\")\n", " ax.errorbar(\n", " d[\"theta\"],\n", @@ -775,278 +641,6 @@ "fig.suptitle(r\"$(\\vec{p},n)$ analyzing power to the IAS at $E_p = 22.8$ MeV\", y=0.92)\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "26d48672", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:02:44.436039Z", - "iopub.status.busy": "2026-09-24T20:02:44.435838Z", - "iopub.status.idle": "2026-09-24T20:02:44.443206Z", - "shell.execute_reply": "2026-09-24T20:02:44.442568Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " theta_1 | Ay(theta_1) | for theta > 40 deg\n", - " target [deg] | data KDUQ WLH CHUQ JLMB | data KDUQ WLH CHUQ JLMB\n", - "--------------------------------------------------------------------------------------------------\n", - " 49Ti 16.6 | 0.52 0.00 0.11 0.25 -0.21 | -0.44 -0.16 -0.07 -0.10 0.11\n", - " 56Fe 15.7 | 0.33 0.03 0.05 0.18 -0.22 | -0.31 -0.20 -0.09 -0.11 0.24\n", - " 64Ni 16.7 | 0.23 0.12 -0.09 0.21 -0.18 | -0.32 -0.18 -0.10 -0.09 0.15\n", - " 70Zn 16.0 | 0.21 0.09 -0.17 0.16 -0.08 | -0.41 -0.23 -0.04 -0.13 0.16\n", - " 90Zr 15.4 | -0.16 -0.11 0.07 -0.04 0.17 | -0.26 -0.14 0.06 -0.06 0.10\n", - " 96Zr 14.4 | -0.20 -0.13 -0.04 -0.11 0.16 | -0.29 -0.10 0.10 -0.01 0.04\n", - " 117Sn 15.0 | -0.38 -0.15 0.01 -0.23 0.09 | -0.08 -0.04 0.08 0.04 -0.00\n", - " 165Ho 16.9 | -0.10 0.01 0.33 0.00 -0.10 | -0.17 0.02 0.20 0.01 -0.02\n", - " 208Pb 17.3 | 0.14 -0.05 0.30 -0.07 0.08 | -- -- -- -- --\n" - ] - } - ], - "source": [ - "# two numbers per target: the analyzing power at the most forward measured angle,\n", - "# whose sign is the feature Gosset et al. tracked across the mass range, and the\n", - "# mean over the measured angles beyond 40 deg, where the data sit on a plateau\n", - "def summarize(theta, curve):\n", - " interpolated = np.interp(theta, angles_deg, curve)\n", - " plateau = interpolated[theta > 40.0]\n", - " # 208Pb has no measured point beyond 40 degrees\n", - " return interpolated[0], plateau.mean() if plateau.size else np.nan\n", - "\n", - "\n", - "models = list(BANDS) + [\"JLMB\"]\n", - "print(f\"{'':>8} {'theta_1':>8} | {'Ay(theta_1)':>34} | {' for theta > 40 deg':>34}\")\n", - "print(\n", - " f\"{'target':>8} {'[deg]':>8} | \"\n", - " + \" \".join(f\"{m:>8}\" for m in [\"data\"] + models)\n", - " + \" | \"\n", - " + \" \".join(f\"{m:>8}\" for m in [\"data\"] + models)\n", - ")\n", - "print(\"-\" * 98)\n", - "for i, d in enumerate(datasets):\n", - " curves = [predictions[name][i][\"Ay_median\"] for name in BANDS] + [jlmb[i].Ay]\n", - " measured_plateau = d[\"Ay\"][d[\"theta\"] > 40.0]\n", - " forward = [d[\"Ay\"][0]] + [summarize(d[\"theta\"], c)[0] for c in curves]\n", - " if measured_plateau.size:\n", - " plateau = [measured_plateau.mean()] + [\n", - " summarize(d[\"theta\"], c)[1] for c in curves\n", - " ]\n", - " plateau = \" \".join(f\"{value:>8.2f}\" for value in plateau)\n", - " else:\n", - " plateau = \" \".join(f\"{'--':>8}\" for _ in range(len(curves) + 1))\n", - " print(\n", - " f\"{d['name']:>8} {d['theta'][0]:>8.1f} | \"\n", - " + \" \".join(f\"{value:>8.2f}\" for value in forward)\n", - " + \" | \"\n", - " + plateau\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "b55768a6", - "metadata": {}, - "source": [ - "Compare with Figs. 4, 6 and 8 of the paper, and with the table above.\n", - "\n", - "The feature Gosset *et al.* tracked across the mass range is the sign of the forward extremum,\n", - "sampled in the table at the most forward measured angle, 15-17$^\\circ$: positive for the\n", - "medium-mass targets and negative for the heavier ones. In the data it runs\n", - "$+0.52, +0.33, +0.23, +0.21$ for $^{49}$Ti, $^{56}$Fe, $^{64}$Ni, $^{70}$Zn and then\n", - "$-0.16, -0.20, -0.38$ for $^{90}$Zr, $^{96}$Zr, $^{117}$Sn. CHUQ reproduces all seven signs and\n", - "KDUQ six, with $^{49}$Ti sitting at zero. Both also get the negative plateau beyond 40$^\\circ$,\n", - "KDUQ at roughly half the observed depth and CHUQ at about a quarter of it. That is the same\n", - "failure mode the paper reported for its own best DWBA calculations: right phase, too small an\n", - "amplitude. WLH does not reproduce the changeover at all, and its band is several times wider than\n", - "the other two throughout.\n", - "\n", - "Two panels should not be read as tests of the model, and the paper says as much: $^{165}$Ho is\n", - "strongly deformed, so a spherical Lane model is the wrong tool, and $^{208}$Pb has four points with\n", - "an exit neutron energy of 3.7 MeV, where the compound-nucleus contribution cannot be neglected.\n", - "They are also the two targets whose forward extremum KDUQ and CHUQ fail to reproduce — flat against\n", - "$-0.10$ for $^{165}$Ho, and the wrong sign for $^{208}$Pb.\n", - "\n", - "The JLMB curve is the interesting outlier: its analyzing power comes out with the opposite sign to\n", - "both phenomenological potentials and to the data, over most of the angular range and for most\n", - "targets. Since $A_y$ here is driven almost entirely by the isovector spin-orbit form factor, and\n", - "JLMB is the one potential whose spin-orbit is a Scheerbaum form factor rather than a fitted\n", - "Woods-Saxon derivative, this says something about the isovector content of that form factor rather\n", - "than about the coupled-channels solution. It is recorded here as an observation, not a result.\n", - "\n", - "The same S-matrices give the cross section, which — as the paper emphasized — is far less\n", - "discriminating. There are no cross-section data in this EXFOR entry; the paper compared against a\n", - "separate Boulder measurement." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "14aaebd4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:02:44.444485Z", - "iopub.status.busy": "2026-09-24T20:02:44.444315Z", - "iopub.status.idle": "2026-09-24T20:02:45.863858Z", - "shell.execute_reply": "2026-09-24T20:02:45.863257Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = grid_figure(r\"$d\\sigma/d\\Omega$ [mb/sr]\")\n", - "for i, (ax, d, curve) in enumerate(zip(axes.flat, datasets, jlmb, strict=True)):\n", - " for name, color in BANDS.items():\n", - " lo, hi = predictions[name][i][\"dsdo\"]\n", - " ax.fill_between(angles_deg, lo, hi, color=color, alpha=0.35, lw=0, label=name)\n", - " ax.plot(angles_deg, curve.dsdo, color=JLMB_INK, lw=1.2, ls=\"--\", label=\"JLMB\")\n", - " ax.set_yscale(\"log\")\n", - " ax.set_ylim(1e-3, 3e1)\n", - " ax.text(0.04, 0.06, d[\"label\"], transform=ax.transAxes, fontsize=13)\n", - "\n", - "axes[0, 0].legend(loc=\"upper right\", fontsize=8, frameon=False)\n", - "fig.suptitle(r\"$(\\vec{p},n)$ cross section to the IAS at $E_p = 22.8$ MeV\", y=0.92)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3b0da5fc", - "metadata": {}, - "source": [ - "## How much does the exact coupling matter?\n", - "\n", - "The first-order approximation to the off-diagonal S-matrix element is the DWBA of\n", - "`jitr.xs.quasielastic_pn`. Feeding both workspaces the same potentials isolates the effect of\n", - "solving the coupled system exactly rather than to first order in $U_1$." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c56a7bdb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-24T20:02:45.865272Z", - "iopub.status.busy": "2026-09-24T20:02:45.865125Z", - "iopub.status.idle": "2026-09-24T20:02:47.613358Z", - "shell.execute_reply": "2026-09-24T20:02:47.612705Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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Mt2/fvowcOZILFy7k+MPgUf77Q2Ts2LEsW7aMcePG0axZM1xdXdFoNPTv3z/bZPCQc/Ca1zxy+mw96vMGBf/M5ff8/97z888/T8uWLVm3bh1bt27l448/Zs6cOfz222/Zlv8UIrckMBSiALy8vHB2diYrKyvH0cb3u9dMHBISYvRH/9SpUzl+GS1btozU1FT69OmT6/KcOnWKunXrZtt/8uRJqlSpkmPzYUhICNWrV39k3jnVKHp5eeHo6JhjTda5c+fQarWPDGIKMhr8Xhly+x7k1qpVqyhVqhTz58/Pduy3335j3bp1LFy48KHBdMOGDYH/D7YKonHjxqxfv55u3brx9NNPs3fvXqMBOg/z/fffA9CpUyfDvnr16rFr1y50Op3RAJRDhw4Zjj/KvebmnGricnLx4kWjGq9Lly6h1+vx9/cH7oyUHjx4sNEI4NTU1DzN/2eKPHIjL5+5B/27MdVn1tfXl9GjRzN69Ghu3LhBgwYN+PDDDyUwFPkmTclCFICVlRV9+vTh119/5fTp09mOx8TEGP6/WbNmANlWsjh9+jQxMTFcvHjR6LzZs2fTqVOnh47qvV9WVhbBwcE5BoYnTpx4YA3GsWPHaN68+SPzv9ekff+XrJWVFR07duT33383mhIkOjqa1atX06JFi2wjX3OTb17k5T3IjZSUFH777Te6d+9O3759s21jxowhISHB0H9w165dOTan3us7l5/atJy0b9+eH3/8kUuXLtG5c+dc9d3cuXMns2bNIiAggAEDBhj29+3bl6ysLKMJqtPS0li2bBlNmjQxCuZzatLMyMhg5cqVODg45FgLnZP/Btn3VoW5F8BYWVlle45ff/21UX/ORzFFHrm9Tm4/cw/6d1PQz2xWVla2oLxUqVKULl26yC5zKYoHqTEUooA++ugjdu3aRZMmTRg+fDg1atQgNjaWY8eOsX37dmJjYwGoUKECtWrVYvv27bz88svAnQAqJiaGOnXq0L17d1577TVSUlKYP38+WVlZOc5pptFoaN26Nbt37zbaf/HiRVJTU7MFhikpKVy6dCnHJqugoCBiY2Pp1avXI+8zMDAQgPfee4/+/ftjY2NDjx49+OCDD9i2bRstWrRg9OjRWFtbs2jRItLS0pg7d26+882L3L4HubFhwwYSEhLo2bNnjsebNm2Kl5cXq1atol+/fowdO5bk5GSeeeYZqlWrRnp6Ovv372ft2rX4+/sb+ozd86D3LzeeeeYZFi9ezMsvv0zPnj3ZsmWL4diff/7JuXPnyMzMJDo6mp07d7Jt2zbKly/Phg0bDANLAJo0acJzzz3H5MmTuXHjBpUqVWLFihWEhoayZMkSo2uOHDkSnU5Hq1atKFOmDFFRUaxatYpz587x6aefZhtM9SAhISH07NmTzp07c+DAAX744QdefPFFw+e1e/fufP/997i6ulKjRg0OHDjA9u3bKVmyZK6fjynyyK3cfuYe9Pku6Gc2ISGBsmXL0rdvX+rWrYuTkxPbt2/nyJEjD5x3UYhcKfRx0EIUE3lZ+SQ6Olq99tprys/PT9nY2CgfHx/Vvn179e233xql++yzz5STk5NhKopt27YpQB0+fFi98sorytXVVbm4uKh+/fqpsLCwbNdJSEhQgOrfv3+2Yz/99JMC1OnTp432Hz58WAFGq2Xc884776hy5copvV7/yHtUSqlZs2apMmXKKK1WazQFx7Fjx1SnTp2Uk5OTcnR0VG3btlX79+/PVZ4PyvdBK5/ce1/+O/1Hbt+D//rv+9yjRw9lb2+vkpKSHnjOkCFDlI2Njbp586b6888/1csvv6yqVaumnJyclK2trapUqZIaO3ZstpVPHvb+Papc9/vkk08UoLp3764WL15sNJWSra2t8vHxUU8//bT68ssvlU6nyzH/lJQUNWHCBOXj46Ps7OxUo0aN1JYtW7Kl+/HHH1WHDh2Ut7e3sra2Vu7u7qpDhw7q999/f+Q9KPX/U62cPXtW9e3bVzk7Oyt3d3c1ZswYlZKSYkh3+/ZtNXToUOXp6amcnJxUp06d1Llz51T58uWNpop52Io4Bc1j8ODBqkSJEtnybd26tapZs2a2/bn9zD3o301uz8+pvGlpaertt99WdevWVc7OzqpEiRKqbt266ptvvsn+JgiRBxqlcjmkTAhRYPHx8VSoUIG5c+cybNgwvvjiCyZMmEBSUlKO/f/+a/PmzXTv3p0TJ05km6g6r9LS0vD392fSpEm88cYbBcpL5I4p37/iYvr06cyYMYOYmJhso32FEEWP9DEUohC5uroyceJEPv74Y/R6PadOnaJChQq5CgrhTn+2/v37mySoWLZsGTY2NjnOByfMw5TvnxBCmIPUGAphQU2aNMHHx+eRaxULUVxJjaEQxYvUGAphIUopzpw5k6upYoQQQojCIDWGQgghhBACkBpDIYQQQghxlwSGQgghhBACkMBQCCGEEELcJYGhEEIIIYQAJDAUQgghhBB3yVrJBaTX64mIiMDZ2RmNRmPp4gghhBDiMaeUIiEhgdKlS6PVmraOTwLDAgoNDaVixYqWLoYQQgghnjCXL1+mQoUKJs1TAsMCsrGxAeDs2bOUKVPGwqUpPnQ6HX5+foSHh+Pi4mLp4hQL8szyR55b3skzyx95bnknzyx/rl+/To0aNQwxiCkV68Bwz549fPzxxwQFBREZGcm6devo3bv3Q8/ZvXs348eP58yZM/j5+fH+++8zZMgQozTz58/n448/Jioqirp16/L111/TuHHjHPO713zs7OwsH+p8cHFxkeeWR/LM8keeW97JM8sfeW55J88sb3Q6HYBZurAV68EnSUlJ1K1bl/nz5+cqfUhICN26daNt27YcP36ccePG8corr/DXX38Z0qxdu5bx48czbdo0jh07Rt26denUqRM3btww120IIYQQQhQJxbrGsEuXLnTp0iXX6RcuXEhAQACffvopANWrV+eff/7h888/p1OnTgB89tlnDB8+nKFDhxrO+eOPP1i6dCmTJk0y/U0IIYQQQhQRxbrGMK8OHDhAhw4djPZ16tSJAwcOAJCenk5QUJBRGq1WS4cOHQxp/svOzs5wrk6nM2xpaWlmuovHg52dHdOmTTM8P/Fo8szyR55b3skzyx95bnknzyx30tLSjGKM9PR0ALM8tycqMIyKisLb29ton7e3NzqdjpSUFG7evElWVlaOaaKionLM896bUrFiRVxdXQ3b7NmzzXMTjwk7OzumT58ufwzyQJ5Z/shzyzt5Zvkjzy3v5JnlzuzZs41ijHuzoZjjuRXrpuSi5L8jquRDLoQQQghTmDx5MuPHjze8vjea2xyeqMDQx8eH6Ohoo33R0dG4uLjg4OCAlZUVVlZWOabx8fF5aN4yokoIIYQQ5mBnZ1doFU5PVFNys2bN2LFjh9G+bdu20axZMwBsbW0JDAw0SqPX69mxY4chjRBCCCHE46pYB4aJiYkcP36c48ePA3emozl+/DhhYWHAnarXQYMGGdK/+uqrXLlyhYkTJ3Lu3Dm++eYbfvrpJ958801DmvHjx7N48WJWrFhBcHAwo0aNIikpyTBKWQghhBDicVWsm5KPHj1K27ZtDa/vtb8PHjyY5cuXExkZaQgSAQICAvjjjz948803+fLLLylbtizfffedYaoagH79+hETE8PUqVOJioqiXr16bNmyJduAFCGEEEKIx41GKaUsXYjiTKfT4erqSnx8vPQxFEIIIYTZmTP2KNZNyUIIIYQQwnQkMBRCCCGEEIAEhkIIIYQQ4i4JDIUQQgghBCCBoRBCCCGEuEsCQyGEEEIIAUhgKIQQQggh7pLAUAghhBBCABIYCiGEEEKIuyQwFEIIIYQQgASGQgghhBDiLgkMhRBCCCEEIIGhEEIIIYS4SwJDIYQQQggBSGAohBBCCCHuksBQCCGEEEIAEhgKIYQQQoi7JDAUQgghhBCABIZCCCGEEOIuCQyFEEIIIQTwGASG8+fPx9/fH3t7e5o0acLhw4cfmLZNmzZoNJpsW7du3QxphgwZku14586dC+NWhBBCCCEsytrSBSiItWvXMn78eBYuXEiTJk344osv6NSpE+fPn6dUqVLZ0v/222+kp6cbXt+6dYu6devy3HPPGaXr3Lkzy5YtM7y2s7Mz300IIYQQQhQRxbrG8LPPPmP48OEMHTqUGjVqsHDhQhwdHVm6dGmO6T08PPDx8TFs27Ztw9HRMVtgaGdnZ5TO3d29MG5HCCGEEMKiim1gmJ6eTlBQEB06dDDs02q1dOjQgQMHDuQqjyVLltC/f39KlChhtH/37t2UKlWKqlWrMmrUKG7duvXIvHQ6ndGWlpaWtxsSQgghhMhBWlpatjjDXIptYHjz5k2ysrLw9vY22u/t7U1UVNQjzz98+DCnT5/mlVdeMdrfuXNnVq5cyY4dO5gzZw5///03Xbp0ISsr66H5+fn54erqathmz56d95sSQgghhPiP2bNnG8UYfn5+ZrtWse5jWBBLliyhdu3aNG7c2Gh///79Df9fu3Zt6tSpQ8WKFdm9ezft27d/YH7h4eG4uLgYXku/RCGEEEKYwuTJkxk/frzhtU6nM1twWGxrDD09PbGysiI6Otpof3R0ND4+Pg89NykpiTVr1jBs2LBHXqdChQp4enpy6dKlh6ZzcXEx2iQwFEIIIYQp2NnZZYszzKXYBoa2trYEBgayY8cOwz69Xs+OHTto1qzZQ8/9+eefSUtLY+DAgY+8zrVr17h16xa+vr4FLrMQQgghRFFWbANDgPHjx7N48WJWrFhBcHAwo0aNIikpiaFDhwIwaNAgJk+enO28JUuW0Lt3b0qWLGm0PzExkbfffpuDBw8SGhrKjh076NWrF5UqVaJTp06Fck9CCCGEEJZSrPsY9uvXj5iYGKZOnUpUVBT16tVjy5YthgEpYWFhaLXGse/58+f5559/2Lp1a7b8rKysOHnyJCtWrCAuLo7SpUvTsWNHZs2aJU3DQgghhHjsaZRSytKFKM50Oh2urq7Ex8ebtc1fCCGEEALMG3sU66ZkIYQQQghhOhIYCiGEEEIIQAJDIYQQQghxlwSGQgghhBACkMBQCCGEEELcJYGhEEIIIYQAJDAUQgghhBB3SWAohBBCCCEACQyFEEIIIcRdEhgKIYQQQghAAkMhhBBCCHGXBIZCCCGEEAKQwFAIIYQQQtwlgaEQQgghhAAkMBRCCCGEEHdJYCiEEEIIIQAJDIUQQgghxF0SGAohhBBCCEACQyGEEEIIcVexDwznz5+Pv78/9vb2NGnShMOHDz8w7fLly9FoNEabvb29URqlFFOnTsXX1xcHBwc6dOjAxYsXzX0bQgghhBAWV6wDw7Vr1zJ+/HimTZvGsWPHqFu3Lp06deLGjRsPPMfFxYXIyEjDdvXqVaPjc+fO5auvvmLhwoUcOnSIEiVK0KlTJ1JTU819O0IIIYQQFlWsA8PPPvuM4cOHM3ToUGrUqMHChQtxdHRk6dKlDzxHo9Hg4+Nj2Ly9vQ3HlFJ88cUXvP/++/Tq1Ys6deqwcuVKIiIiWL9+fSHckRBCCCGE5RTbwDA9PZ2goCA6dOhg2KfVaunQoQMHDhx44HmJiYmUL18ePz8/evXqxZkzZwzHQkJCiIqKMsrT1dWVJk2aPDRPAJ1OZ7SlpaUV4O6EEEIIIe5IS0vLFmeYS7ENDG/evElWVpZRjR+At7c3UVFROZ5TtWpVli5dyu+//84PP/yAXq+nefPmXLt2DcBwXl7yvMfPzw9XV1fDNnv27PzemhBCCCGEwezZs41iDD8/P7Ndy9psORdBzZo1o1mzZobXzZs3p3r16ixatIhZs2YVKO/w8HBcXFwMr+3s7AqUnxBCCCEEwOTJkxk/frzhtU6nM1twWGwDQ09PT6ysrIiOjjbaHx0djY+PT67ysLGxoX79+ly6dAnAcF50dDS+vr5GedarV++hebm4uBgFhkIIIYQQpmBnZ1doFU7FtinZ1taWwMBAduzYYdin1+vZsWOHUa3gw2RlZXHq1ClDEBgQEICPj49RnjqdjkOHDuU6TyGEEEKI4qrY1hgCjB8/nsGDB9OwYUMaN27MF198QVJSEkOHDgVg0KBBlClTxtDfb+bMmTRt2pRKlSoRFxfHxx9/zNWrV3nllVeAOyOWx40bxwcffEDlypUJCAhgypQplC5dmt69e1vqNoUQQgghCkWxDgz79etHTEwMU6dOJSoqinr16rFlyxbD4JGwsDC02v+vFL19+zbDhw8nKioKd3d3AgMD2b9/PzVq1DCkmThxIklJSYwYMYK4uDhatGjBli1bsk2ELYQQQgjxuNEopZSlC1Gc6XQ6XF1diY+Plz6GQgghhDA7c8YexbaPoRBCCCGEMK1i3ZQsir97SxPe4+vrazQiXAghhBCFR2oMhUUtWrSIwMBAw7Zo0SJLF0kIIYR4YklgKCxq4MCBDBo0CLgzinzgwIEWLpEQQgjx5JLBJwUkg0/yLzExkaZNmxIcHIxer0er1VK9enUOHjyIk5OTpYsnhBBCFEky+EQ8lubNm2cICuHOBOXBwcHMmzfPwiUTQgghnkwSGAqLCQkJwcrKymiflZUVISEhFiqREEII8WSTwFBYTEBAAFlZWUb7srKyCAgIsFCJhBBCiCebBIbCYsaMGUP16tUNq9Pc62M4ZswYC5dMCCGEeDJJYCgsxsnJifXr1xtGIg8cOJD169fLwBMhhBDCQmSCa5Enpp6Q+ocffmDlypUArFy5koCAAKZPn17QYgohhBAiHyQwFHny9ddfM3v2bMPryZMn87///S/f+Y0cOZKePXsaXsuqJ0IIIYTlyDyGBfQkzWOYmJhIw4YNuXjxomHewcqVK3P06FFp/hVCCCEKicxjKIqEefPmGYJCuDPv4MWLF2XeQSGEEOIxIU3JItfuzTt4LzCEojXvoKn7PwohhBBPGqkxFLlW1OcdXLRoEYGBgYZt0aJFli6SEEIIUaxIYChyrajPOzhw4EAGDRoEwKBBgwzT4AghhBAidyQwFLlWlOcdTExMpHfv3vzwww/AnWlwevfuTWJiooVLJoQQQhQfEhiKPPnvvIP3AjFLmzdvHsHBwUYDY4KDg2VgjBBCCJEHMvhE5ElRnXewqA+MEUIIIYqDYl9jOH/+fPz9/bG3t6dJkyYcPnz4gWkXL15My5YtcXd3x93dnQ4dOmRLP2TIEDQajdHWuXNnc99GseHr60uDBg0MW1EJDIv6wBghhBCiOCjWgeHatWsZP34806ZN49ixY9StW5dOnTpx48aNHNPv3r2bF154gV27dnHgwAH8/Pzo2LEj169fN0rXuXNnw9QnkZGR/Pjjj4VxO6IAxowZQ+XKlY0GxlSuXLnIDIwRQgghioNiHRh+9tlnDB8+nKFDh1KjRg0WLlyIo6MjS5cuzTH9qlWrGD16NPXq1aNatWp899136PV6duzYYZTOzs4OHx8fw+bu7l4YtyMKwMnJiWeffdaoj+Gzzz5bJAbGCCGEEMVFse1jmJ6eTlBQEJMnTzbs02q1dOjQgQMHDuQqj+TkZDIyMvDw8DDav3v3bkqVKoW7uzvt2rXjgw8+oGTJkg/NS6fTGb22s7PDzs4ul3cjTGHs2LH07dvX8LqoNHMLIYQQBZGWlkZaWprh9X9jDlMqtoHhzZs3ycrKwtvb22i/t7c3586dy1Ue77zzDqVLl6ZDhw6GfZ07d+bZZ58lICCAy5cv8+6779KlSxcOHDiAlZXVA/Py8/Mzej1t2jSmT5+e+xsSBSYrnQghhHgczZ49mxkzZhTKtYptYFhQH330EWvWrGH37t3Y29sb9vfv39/w/7Vr16ZOnTpUrFiR3bt30759+wfmFx4ebrSQtdQWCiGEEIXncV4WdfLkyYwfP97wWqfTZauQMpVi28fQ09MTKysroqOjjfZHR0fj4+Pz0HM/+eQTPvroI7Zu3UqdOnUemrZChQp4enpy6dKlh6ZzcXEx2iQwFEIIIQrP47wsqp2dXbY4w1yKbY2hra0tgYGB7Nixg969ewMYBpI8bCTq3Llz+fDDD/nrr79o2LDhI69z7do1bt269dj86hCP9jj/6hRCiMfVyJEjadiwIT169GDjxo0EBgZaukikZGRxOOw2e67Ekp6pZ2Sz8pR1c7B0sR6q2AaGAOPHj2fw4ME0bNiQxo0b88UXX5CUlMTQoUOBO+vllilThtmzZwMwZ84cpk6dyurVq/H39ycqKgq4M6LVycmJxMREZsyYQZ8+ffDx8eHy5ctMnDiRSpUq0alTJ4vdpyhcixYtMurLIf1FhRCi6PP19aVWrVoA1KpVy6I/6DedjWbOzkscDosjPev/F1749O/LvN2mEhPbVqSEXdEMwYpmqXKpX79+xMTEMHXqVKKioqhXrx5btmwxDEgJCwszzGsHsGDBAtLT041GrsL/f/FbWVlx8uRJVqxYQVxcHKVLl6Zjx47MmjVLmoafIEXxV6cQQojiYeH+UF77+Rguty7imp7IrbKNqV/GlRux8aSf38ustHS+OxTGpz1r0L9+GUsXNxuNUkpZuhDFmU6nw9XVlfj4eLO2+YvCFRoaSkBAACEhIfj7+1u6OEIIIXLBkn+7lVLM3HqBDzYdo9Kp1ZRIjMTG1o7NW7fj7uTAJ599zprVq0h38OBqpS7oPCrx86BA+tYtnedrmTP2KNY1hkIIIYQQlpalV7y+7jRLth+l2qlV2KXG0fypFvTt8yzO9jYADB08iERdPJs2baLyqVXcKlWbET9Z0bS8e5Hqd1hsRyULIYQQQhQF4zecYcWfe6j27xLsUuN4/vl+fP7Zp7Rq1Qpr6zt1cCVLlmT69Ol89913lPP3p+SNU9hc/Icha46j1xedxlsJDIUQQggh8mnjmSi+2nMFv8tbsc5MZdy4cbz99oQHLopRr149vpk3DzsHR8qE7OCfE+f5fM+VQi71g0lgKMR/JCYmsmDBAuDOgKXExEQLl0gIIcSjWOJvd6QulZfXngCNhrI9RzFnzlwGDhyIRqN56Hk+Pj68/+5kAtr1Jc3Bg3c3n+NERLzZy5sbMvikgGTwyeMlMTGRpk2bEhwcjF6vR6vVUr16dQ4ePIiTk5OliyeEECIHlvjbrdcrOn27n11nr+Ph7srJt1rj42L/6BPvk5KRRaMv9nImKoGaPs4cH98Ka6tH19mZM/aQGkMh7jNv3jzDHxa4M2l6cHAw8+bNs3DJhBBCPIgl/nZ/vucKp3ZsoEbQQj5sXCLPQSGAg40V379QF++Iw4SeP8tPJyLMUNK8kcBQiPuEhIRk6xdiZWVFSEiIhUokhBDiUQr7b/fx6/HM+vEvyoTsxFGbRY/ASvnOS3PzKmUv/kn5CxuYs+Milm7IlcBQiPsEBASQlZVltC8rK4uAgAALlUgIIcSjFObfbr1eMXL1IfxO/4oGxQczpuPj45Pv/OrVq0eDpk/hkHyTq6ePsuXcDROWNu8kMBTiPmPGjKF69eqGFXPu9VN52PrbQgghLKsw/3avOBpO5N7fsEuLp3WXXnRo17bAeY4d+QoAPuH7+WjnpQLnVxASGApxHycnJw4ePMiECRMAmDBhggw8EUKIIq6w/nbHp2QwZfV2vCKOYuPswQfvTjBJvrVr16ZCtVo46cIJ+vc4B0JjTZJvfkhgKMR/ODk5MWrUKABGjRolQaEQQhQDhfG3e/rW8yTfikZZ2fL2W+NxcDDdiiVjRrwM3Kk1nGPBWkMJDIUQQgghHuFMVAJf/xPK7VK1eH7qPJ7p1smk+bdo0QJf/4qk27nw++kogqMTTJp/bklgKIQQQgjxEEopxv58jKwsPTV9nHmrc71HTmKdV1qtlt9+XIVLy36g0TB312WT5p9b1ha5qigS9HqFLi2T2OR0bidncCspjdCQK+iSkklMSiY5JQ29PousrCzsXTwoWa4yttYa0uJvkRIbRQkHBzxcnfFyc6WUhytlPV3xdnHASmvafyyFLTIyktOnTwNw+vRp7Ozs8PX1tXCphBBCWMr601Fc3rqaqkk3mPn8h9jkYhLq/LCxseadthUZ9ONxVgWF83H36ng62ZnlWg8igeFjSilFpC6Nq7eTuRafysnTZ7h04TwxN6LRxd4kNT4WfXIc1unJnG48hiwbRzT6LBrs/SDH/GK9ahBS4zkASoUfwO/K1uzX1Gi5VPclHMtWxcfZDrfLf1PSyZ5yfmWoVqE8TWtVoaafV5EPHBctWsSMGTMA6NGjB9OmTWP69OmWLZQQQgiLyMzS8+6qHXhGBmHt7EHXehXMer1W3hqqn11DvL0Xq47V5I1W5r3ef0lgWMylZWZxLjKeg2cucvLcJa6EhBATcY2UW5HEelQhqnwrAMpe+gvv6wcN59kCeo0VmTaOWGWlk2XjiJ2tDYll6mNlY4u1rR3WtnZoNFq0Wi2l3H3wKedOepaeDKuKpFu3IjM9ncy0FPTpqVhlpmKdkUy6dQl0CWlEJaRR99hWEjJTCQX2AN8C6fau2PjVpGaPodQv40IDXycCy3ngZG9jgaeXs5EjR9KzZ0/Da6ktFEKIJ9fSw+FkBW1AA7w2Ziz29nlf4SQvSrq74RQXig3hLDkYwustA0zebP0wEhgWI7eS0tlzNpS9R08Q6RTAv9d1XImMofa+T9BgPFO6A2DnWAqNBryd7PCu2RC3iv54e3vjV9qHSuXKULG0F6Wc7fFwtMHdwQZ7GyugWy5K0srolV6vSEjLJCYpnShdKlEJaUTEp3Iq4C3Crl/nRlQkibeisU6IwT7pBrfj4vg+6BrfB4FnRBBlQrajLemHX+UatGjaiOfaN8Pfy9Vkzy2vfH19JRgUQohixhzdgJLTM5n9wyZK3r5CCd8AXnq2uymK+lBOTk40bdma/Tu3cunMMYKuBdLQz83s171HAsMi6nZyOjtOhbLjwBHOnztPTPgVNLevY5t+Z5TS6UZjSHMsCVYOJLmUAQdXXL3LUMavHFUrVaBhjcrULueFv7sjttbmHWOk1WpwdbDB1cGGSp4l/v/AfdXfSimuxaVyKjKeE1dvci4ug6Phcdy8nonSWKGNvsj16Ius/ed3fvxUi96jHA37vUbvpjVoW7EkJezkoyqEEOLBzNEN6Iu/L+Nw5k8A3p/4VqHV3A3u14f9O7fiGXWcpYfDCjUw1KgCLsr36quvMnPmTEqVKmWqMhUrOp0OV1dX4uPjcXFxyVceWXrFifBY/th/jJPXYjmtL8W5G4n4hO2lTMjO/09nZUeKsw+O3uWp0aorTWtWpI6vC9W9nfAsYVuoVc2mkpCaSdC12+w8fpF/Dh8l/PwZ7GJDsU2L5/hT76C01tiRSYNrf9HsqRaMeLYzVct6WrrYQgghipjIyEgiIyMNrwva+nMrKZ2aE76j7JGllKpan82rFpuimLmilKJd5+7obt0gtM1Ers1+Fgeb/18L2hSxx4MUuBqmS5cudO3ale7du/P2229TokSJR5/0hEtKy2TnmTD++PsgJ08cJ/7aReziI9CqLJKcS3OuwXAAkj2roHGEipWr0bhuTVrVrUzt0q7YWVs94grFh7O9NW0qedGmkhf0bX4nSI6IZ/PxEDyupbL3Six2N0JIv3yUvy8fZdf3X6P1rUyDZi15Y0BvapbztvQtCCGEyCdTBnOm7gY0e8dFoh390DZ7lRVjC77sXV5oNBr69O7J8iWLsQ47xm8nmzEgsGzhXLugNYZwZ6Hqb7/9loULFzJq1ChGjBhhWK/Q3ObPn8/HH39MVFQUdevW5euvv6Zx48YPTP/zzz8zZcoUQkNDqVy5MnPmzKFr166G40oppk2bxuLFi4mLi+Opp55iwYIFVK5cOcf8chO1305OZ8vxyxy+lsDBqHSOhsdR5dB8HJJjDGlS7d3B0x+/KjXo1L0Xzcq7U6+My2MVBOZHUlom2y/c4Kdt+wg68A82kWewS40D7gyecWg/jMG9OvFcXV/cHW0tW1ghhBB5Mn36dEPzL1BkZoG4GptMlY92kZ6l5/NeNRlXyCODAaKiouj/+nsEOdWlcaNG7BjVzHDMnDWGJgkMAcLCwti5cycTJkzA09OTjz/+mB49epgi6wdau3YtgwYNYuHChTRp0oQvvviCn3/+mfPnz+fYtL1//35atWrF7Nmz6d69O6tXr2bOnDkcO3aMWrVqATBnzhxmz57NihUrCAgIYMqUKZw6dYqzZ8/mOBIppzcnOiGNTYeD2fbPIS6cOUF65CXsU2IJq9iZmLJNAPAN30cZu0xq1K7D0081ol2tAEq7mnekU3Gn1ysOXo3l++1H2L1zO1bhJzjX4BWybByx1ULz+AP06tiOkT1b42BbdEY5CyGEyFlkZCRBQUH06NGDjRs3EhgYWCQG/w1c+g97fl+NVe0OnJvey2KVNDsv3qT9wgMAXH63HRVK3mmVLdKBYefOnQkODsbPz4/GjRvTqFEjqlSpwjfffIOzszNffPGFiYqaXZMmTWjUqBHz5s0DQK/X4+fnx9ixY5k0aVK29P369SMpKYlNmzYZ9jVt2pR69eqxcOFClFKULl2at956y7AQd3x8PN7e3ixfvpz+/ftny/P+N+d8nJ6XFu/AZuc32KXFG9LoNVpSXMriUa8N7Tp0pFWFkjTzd8dJBlTkm16v2HPlJquORfDziQgyo69Q7fgyADLs3SjfsBXjXx5AmzoVLVxSIYQQDxMaGkpAQAAhISH4+/tbujicitTRddR7eF87QOOuz/HNzHcsVha9XlFx9g5CbyUzpWMVZnauBhTxPoYfffQRtWvXxsrKOJpesmQJ1apVK2j2D5Senk5QUBCTJ0827NNqtXTo0IEDBw7keM6BAwcYP3680b5OnTqxfv16AEJCQoiKiqJDhw6G466urjRp0oQDBw7kGBjeo9PpcNbacT7JmjoqiySPCnhVqE5g/Qb0aN2Ipyp5P/HNwqak1WoMfRO/fqYWv58MZ+k6a64c3YvzrQtE/LOBt/7ZiNavJi8OHc6rXZoaddwVQgghcjJxzT68rh9G2ZVg9oTRFi2LVquh8c1/cDq8l2VO4xnfzBetRoNOpzPbNQscGNarV++BxzZv3lzQ7B/o5s2bZGVl4e1tPPjA29ubc+fO5XhOVFRUjumjoqIMx+/te1CaB/Hz87vzP7U7cTL6Gi91CWDYC+3u7Iu+yMHoi7m6L5E/PsC7T1dD17oqWy/FsfPAYTQhR7EPP83kPy8wbX8snf1s6eidSWUvJ0sXVwhRzMSn6/n3ZhY3UvQkZCgS0hVJmeBhp8HfWYu/sxXlnbU4Whe/2SEs7d7gk4MHD3L16lWLluXkrUzObfuZkiqL+i3bc/zfYxYtD4BT6k3s0uJJCjmJe+3JEH7KrNczSVumTqfj+PHjHD9+nNdff92wv0KFwu+saSnh4eFG1bl2dnbY2RXu+obijh5Pg3q1FwdCY/ly/R7ORNmSkKFnw+kILi1fiK1/XZ5//jnGPNMOW6nFFUI8wKlIHb+djGRz0HlCTgfhHHsF29Q4rDNTOdPoNZTWCpSeSqd+JNXRg9QSpagd2JRRHerQq6aP2eeQfVyEhoYCd7p2WbIpWSnFO9PX4nHjJBpnT+bPnIStreUHNZYsWZIhQw5SMuo4XT/8jm96VUOn0/1/hZSJ5TkwDAsLMwSB97arV6+ilKJEiRJGgaE5eXp6YmVlRXR0tNH+6OhofHx8cjzHx8fnoenv/Tc6Otqo82t0dPRDa0YBXFxcTN7OL/JPo9HQPKAkzd98htjkdFYevcbiddvIsHVCG3KM1XOOsWKBL407dGfK8P6Us+BKK0KIouWfK7f4cMdFju7cTKnrR3BIjqHcfcc1Nnb0qeaGu4c7oVfDiN1zCdfbd47FX/iDSduqMqFCU17o0pa32lbCy0kqCR4kMTGRBQsWALBgwQKmTJmCk5NlWnV+Px1F7P7fcAGGjni1SASFADVr1sTNuwwq+gqbjoew6PkGmDPcyPXPmXbt2lGyZEn8/f0ZPHgwf/31F15eXoSFhbFkyRKuXr1KQkKC+Ur6H7a2tgQGBrJjxw7DPr1ez44dO2jWrFmO5zRr1swoPcC2bdsM6QMCAvDx8TFKo9PpOHTo0APzFEWfh6Mt41pV4PRnI/hy2Wq8e4xG514RG10k//62mJ49uvPCgr84Gh5n6aIKISxo16WbtPx6Dy3n72fLuRhAi33KTVzLVaFLvyEsW/E9Bw8e5MiBffw8sg3fPleXv97qzpYtW/jk86/o0PclbFxK4n7zHF6Hl7Ps+x+oNmcXK46EY6IJQB4riYmJNG3alE8++QSATz75hKZNm5KYmFjoZcnM0vPu5mBiS9XCoUI9RvXvVehleBCNRkOfXt3RoLALP8H60w/v2lZgKpdsbGzUu+++q65du2a039raWp05cya32ZjUmjVrlJ2dnVq+fLk6e/asGjFihHJzc1NRUVFKKaVeeuklNWnSJEP6ffv2KWtra/XJJ5+o4OBgNW3aNGVjY6NOnTplSPPRRx8pNzc39fvvv6uTJ0+qXr16qYCAAJWSkpJjGeLj4xWg4uPjzXuzwqSuxSWrcSt2qCrPjlbV2vRQvPm7YvwG1XjuX+p/329SqemZli6iEKKQ3EpKUwOX71dle45WNVp2Ury5XrWev09tPnVNxcbG5imvrKws9efOParnkNHK++21ivEbFOM3qLafb1cXbiSY6Q6Kp9mzZyutVqsAw6bVatXs2bMLvSzfHbyqGL9Bad/aoIKjdIV+/UeJiIhQgYGBqnrrbqrjwgNmjT1y3ZR86NAh3njjDc6cOcPcuXOpUqWKmULV3OvXrx8xMTFMnTqVqKgo6tWrx5YtWwyDR8LCwowm2m7evDmrV6/m/fff591336Vy5cqsX7/eMIchwMSJE0lKSmLEiBHExcXRokULtmzZkuMchqL4KuPqwOeD2vHRi61Z++915u27ypHwOEKP7ObXK1tZs2Q+9dt2Y+qIF6ng427p4gohzEApxS8nI5mw8BdcT67HO02HxsGFX58tz7Mt6uUrT61WS+e2LenctiU3E9OYsPEsv27/h9v//EibQ+344I3hDG1a3rQ3UkyFhIRgZWWFXq837LOysiIkJKRQy5GSkcXMX/9Bo3dkWPMAqnk7F+r1c8PX15dmHXuy9kom5y/cICI+wGzXyvM8hqtXr+a9996ja9euTJs2jTJlynDixAlq1KhhrjIWaeacS0gUrkNXbzN37V+c2vE7LrfujCLPsrbDo3YLXn/5JXo2rVks16MWQmSnS81g6Ip9HPt9BSVvnEKhoU7rLnw9Y6LJ+7h9vHQtaxZ9iSYrndueNXhmxJvM7V0PrfbJ/nvy0Ucf8d577xkFhlqtlg8//DDHuYjNZfZfZ1k9cyxY2bDh1zUEeLkV2rXzIjNLT7kPthOpS2Nm27JM7dGg6ExwnZyczP/+9z8WLVpEbGwsx48fp3bt2iYtWHEhgeHjJzohjc82HWTDb79iHxaEVVYamdYOaPtMZ2zryvSvX0bmRBSiGDsblcCz3+3D5s+PsUuLx8bdh//NmEbb5o3Mds3LV8MZOPJ1Mm6Gk+TkS6U+Y/lxRLsneqGDe30Mg4OD0ev1aLVaqlevzsGDBwttAEpscjoNhs/E8/wWyjRow+/fflIo182vtzee5ZNdl6jmpuHctJ5miT3yNZbe0dGRDz74gEOHDtG9e3fat2/PJ598QkpKikkLJ4QleDvbMeeF1pxc8wXjv1iBdZO+RJZvxb9Ryby89gSVxszn2bc/YufJS9KhXIhiZu2/12n85V7O384gvmwgDTr04u8/fjNrUAhQsbwf239dhVe1QEokRnL1x9m0mLmWKF2qWa9blDk5OXHw4EHDSmMTJkwo1KAQYNam47hd3o1ea8OnU8Y/+gQLq5ZyiVqHvuTahbNmu4ZJ1kreunUrb775JrGxsYaJKp8UUmP4ZPj3Wjzz9oWw+th1yh5bievtyyg0qNLVaNuxKxMG9MDXXSbOFqKo0usVE9cfZ8XP67npG0hFzxL8NqQRdUoX7t9tvV7PqCkfcXjHH5yrN4wKFQLYNao5pZyf3CltLLUkXvjtFFoMewevsP3UaP8MK+e8V2jXzq+9e/fy5ptvEuNRnbBtPxSdpuScZGZmMm/ePMaNG2eK7IoNCQyfLLeS0vlu1ynWrt9A+vmD2KfcAiDTxhGP2i15a9zrdKrqhbWVTGx7jy41g7DbKUTqUrl2M46wyBtE37yF3rUMiXotutRMYv/dQWZ6KlmZmWRlZqKUQmtlhZWTB3bVmuNgo8UpMxGr22G4Orvg41mSiuVLU8nXEz93R8q62sszFw+UmpHFwCV7OLPmM0okRuLWbjC/zHgVNwcbi5Vp/eHzvPjbJVIy9NTycWbXqGZ4PqHzHVoqMHzp2+2cWfwe2NizbfMGSroV/flsMzMzad2hIwkJCZw6drRorpVsyMja+okLCsWTp2QJW97pHsg73QP591ocX63bxf4df+IYcYrQ8Gt0X3IYLydbupSGTlVL8XzLuoaAJTIy0qhG3dfX12gi9eIqJSOLizFJXIyO5+yVcK7HJxOtceXq7WRizhzCMfQgtqnx2KQnolVZhvOCGwwn2bk0AHWCtmCTkQQY92/Rufhx3u7OmuvuN05TIfhXo2vrtdak27kQVbkzPtUbUMPbibLoaFy1HK2rl8XXRWYTeNLFJqfT6/NNxP35DSVS4/Cs2oBfpr6MkwWDQoDejauywc2DXov2krJ9MR1uhbFjSn9Kligakyo/7k5G6Phz72HKAa17vVAsgkK4E2t16tiRdT+vNd81zJazEI+5+mXdWDb2GdJG9eS3Y6Gs2H+Rq9cyiElMZ8+G9ZyJPsFHzj6UqdmQp9u0IjJoB5989D/D+dOmTWP69OmWu4E8UEoRnZDGuRuJnI9JJDhKx7+7NnMr8hrpt6OxT7mFTZoODXDbszpXaj4PgFdSIt7xYWRZ2ZLh4IbGwRnbEi44OLnQpU55vHxK42JvTWK5kWiVHltbG2ysrbHSasnMzEJjZ49H+WokpWcREWbNdfdMkpMSSdLFkaq7jUqOwzZNR5rGhjNRCZyJSqDGkW/4OzmGWY6eULIcfhWr0qpZE55rVY9KniVkZPkT5GpsMt1mr8F671LsMlOp3rILyz+ZjpVV0Rg81qGKFx/U17Lq7/Nk7QrlaSstu97vh6uFg9bHnVKK8RvOcKtUbcpUrMbscd0tXaQ8ee6ZXmYNDE3WlPykkqZkcb8oXSq/nYpi9S+/cvPkPkrEh3EvDEnRW5Hu5MmlvZuZvuB7XujUiioB5R6aX2FLycji8s0kToXF8O+5i1y4HML18DB0NyKwSrzJlZrPk+bgAUpRd/9crDPvdJzPsrIjs4QHti6euJevSu123Snv7oivvcLP1Z7qfl64OdiYPCjLyNITEZ/C+RuJnItJIjg6kX9/X07CtUvYJ0ahUf8/DYbOLYCMtiPpWMWL3rV8aF/ZS9ayfYxdiEmky4yVuB5agVZl0aH/MGa/9WqR/GEw/eslbFqxgExre0o+M4EtE3pi84R0jYiMjCQoKIgePXqwceNGAgMDzd6SsuF0JL2WHQVg+8imtK/iZdbrmZpSim69+/DnhnVFu4/hk0oCQ/Eg0QlprDlwjj92/E3o6WM43ryIzsqZkAN/wbDFOJNK5VvHKB1QmZrVq9IqsA5PVS9v1toCpRTxqZlcj0/lUmQs/56/zPnLoUQmZRLqWIGwuBQ8rx2i3KUt2c9Fw+W6A/GuXJtqpZzwuBlMhdKlaFSjMg2r+BWp/lGZWXqOh93izwP/cijoX64GnyDWrhQRFdoD4BlxFM/bF6jcoBkDenXm2YaVnpgv4ifByQgdTy86QGzsbaqdXsWrw1/mtRefsXSxHuqtj+bx9y/LSbN3p8GwqSwf0qJIBrGmNn36dGbMmGF4be6WlPRMPfVf/5z0S0eo3Hkgm8cXr9rCe3799Vf69u0rgWFRJIGhyI30TD1/X4rhxy17Wfbmc1iP+A6vm2cofXWPUboMmxJkOZXEsUV/KlSoSHl3B/SRF/Fyc6Z0KU98SrpTwtGOEvZ2ONhYodFoyMjSk5GlSM/MIjYhiZjbOm7F6bgZpyPiRgyp7uWJTLXienwKas9yrBNjsElPMNT2ASS6lOV8/WEAuMRewi98Dw4lfShV2o8K/uWpU7UiTWtVpqqPe7GsZVNKERydyLYLMfwRHM3l3xfhfuP0nWMaLane1WjSthNvvdCNmqXdLFtYUSCHw27T5Zu/ic2wwsvJlr9eaUR9Pw9LF+uRlFL0f/09Lh/YSqJzWV54+wOmda316BOLucLue/3JtrOsnD4Gm7QE5nyzhA6N65jtWuZkzthDAsMCksBQ5FZiYiKzZs1i7ty5jH9rAh0GjmbPmSucPB3MtdDLpMaEY590A5uMZE41fp10B3dQivr//A+tPtMoLwXEl6zK5Vr9AfCMOEL5i5tzvO75uoNJdPMHoObhedilxJJh50yWvQt2bl54eJfBv2JFWrZqQxWvElTxcnrsO8DfTExj6ZaDbPprK7eDD2GXGgdAiqMnbn3fZVzrSjxTy0dGOhcze6/cYuB7H+Madpj4lq+ydXxXqpYqPtNIZWRk0HngSK4kwtUqPVk5sCEDA8tauliPjVtJ6TR++T3cL+/CN7AdGxfNtXSR8k0CwyJMAkORG7mZ4T85PZPzN5I4e+0GEUkQEpdCWGwyN/atJyUxnoykBFR6Chp9Jlp9Jomu5Qiv3BUAt5gz+F7di97KFq2dA1a29tjYO+Lk6kb5Bq2oGFCesq72lLTJpIK3B+U8Spilz19xFBGXzGc/b2Pbn38Qm2XDtUqdAaiguU2/uj6807etDAYoBnZfimHIu3PwvLIbvW0JPv3yK9o2qmvpYuVZUnIKz606wZ/nYrCx0rDntadoWl7WazeFkSv+5sj8d1Baa9b99hv+pUtZukj5JoFhESaBocgNU60JqpQiI0uRlqknNfPO1C/WWg02VlqstRrsrLUS7OWTUop9IbHM2xfKLycjCTjxA66xl0h296dN7/7MGtKDkiWKTj9K8f92XYxh6Luz8QzZg97emfnz59Osbg1LFyvfEtMyeerrfYSeDsLTDvZ9PBofmXqpQE5G6HjmlbG43zhDo95DWPD+GEsXqUDMGXtIO4kQhSAkJCTbFBlWVlaEhITkKR+NRoOttRZne2u8nOzwcrLD3dEWJztr7O/2ORT5o9FoaFGhJGteCuTKu+1o3r4LKW5+ON4O5fCyj2jV6wVenfcLt5LSLF1UcZ8dF2IYOvlDQ1C4cOGiYh0UAjjZWbP8mYpUPPszrv/+RJ8vNpCeqX/0iSJHer1i5OI/cb9xBr2LN5+/PcLSRSrSJDAUohAEBASQlZVltC8rK4uAgAALlUg8TDl3R1a8M5j9G9fS4pX3SHUvj0NcGEeXf0STl8YzZ+clUjKyHp2RMKsdF2J4/rOf8Qz9B72DK4u//ZbGtapYulgmUb9iWfoOew2tPhPd1sW8sfawpYtUbK04Gs5BnSMXaw/gzYmTsLd7vPtQF5Q0JReQNCWL3MhNH0NRdOlSMpj2/R9s+/l7Qsu3I8m1HH5u9kxtW56hzStjpZWa2sK2/UIMPZYcJjVTT63E06wc9zz1q1e0dLFM7vkxk7hycDvxHpV4d9ZHDG3ib+kiFSuxyelU/WgXN5PSGdrIj6X961m6SCYhTclCFHNOTk4cPHiQCRMmADBhwgQJCosRFwcbPh/Rm30b1vD6s+2wt9YSeeMW8yYMo/FLb7HjdLili/hE+etcNP3nriY1U08Nbye2fzL+sQwKAX74bCZ2PgG4xl5i2sdfExQeZ+kiFSvjV/2N/YkNeNhkMad7dUsXp1iQwFCIQuLk5MSoUaMAGDVqlASFxZCrgw3/61qdi5Pb8ay/FUprheb8HsYPH0iv9+cTEZds6SI+9v48G8Wr70zH//j31Ek4ya5RzfF2fnwHBdna2vLDwq9Q9k54XDvEs4t2E5No2X6ukZGRHDt2zLDdPw9hUXIwNJb9P32H9/VDDPaOxasITcJflElgKEQhiYyM5PTpO5Mqnz59usj+MRWPVtbNgbVvPcfCFauxqtkW68wUrm9ZxtN9BzLtx51k6aWHjjlsPB3Ja5OnU/L6EZRTSVa/M5BSj3FQeE9AWV9mzZ7LtaavEpZiRb/vg8jMstxglEWLFhEYGGjYFi1aZLGyPEh6pp5Rn63E5fZl8CzPnLGDLV2kYkMCQyEKyaJFi+jRowcAPXr0KJJ/TEXeNKtcmoPL5zJ42pekelbEIS6MdV/PpNkXuzkVqbN08R4rPx0L541J7+Nx/SjK2ZPvl35HzYrlLV2sQtO1ZWOWj3gagN0Xopmw7l+LlWXkyJFs3LgRgI0bNzJy5EiLleVBpv9+FP2R31AaDVPenYyNtdWjTxKADD4pMBl8InKrsJd+EoUrPiWdUZ+tZMuFWG57Vsdaq2Fiq3JM7VITO/lSKpAVh0KYOX0a7jFnwdWbH5ctpnK50pYulkW8++th1s37gHQ7V2Z9OJsXH7Ayirn/3oSGhhIQEEBISAj+/v4my9cUTkTE02fYWNxizlKxTW/WfvK+pYtkcjL4JAexsbEMGDAAFxcX3NzcGDZsGImJiQ9NP3bsWKpWrYqDgwPlypXj9ddfJz4+3iidRqPJtq1Zs8bctyOeAL6+vjRo0MCwSVD4eHF1sGX1e6+weeZwavo4k5WZwfqP36HxgHHsOCODU/Jr8cGrDF91EMeESDQeZfn1+2VPbFAI8H6X2rjZWeF+M5h3Pp7HyYica6aLQ3OvOWRk6Rny2RrcYs6id/VlyawJli5SsVNsA8MBAwZw5swZtm3bxqZNm9izZw8jRjx40sqIiAgiIiL45JNPOH36NMuXL2fLli0MGzYsW9ply5YZfm1FRkbSu3dvM96JEOJx0rS8O8febMWEhu5YZaVjc/kAb44YzIC5P6BLzTDrtYvLoIDc+mrvFUb8fJIMGyc8e41jw+pllC/Gy5iZgqOjA8u/+RJl60ipSzt4bu5qbienZ0tXHJp7zeGjnZc4rvfhesWOvD91Kk4OsmJMnqli6OzZswpQR44cMez7888/lUajUdevX891Pj/99JOytbVVGRkZhn2AWrduXa7ziI+PV4CKj4/P9TlCFFRERIQKCgoybBEREZYuksjB0ctRqtGAcapBYKAKDAxUNXq+rH4+dN5s15s2bZoCDNu0adPMdi1zm7nxuPLvMkTZvLZKdfn2oEpOz7R0kYqUNZt3qgaBDVXdJi1Ux0//UFlZ+mxpQkJCFKBCQkJMeu2EhAQ1ceJEBaiJEyeqhIQEk+afXyeuxyubtzcqxm9Q7/5x1tLFMStzxh7FssbwwIEDuLm50bBhQ8O+Dh06oNVqOXToUK7zudc2b21tbbT/tddew9PTk8aNG7N06VJULrph6nQ6oy0tTZbNEubzpDYTFTeBFbw5uPIzur/xAWklvHC4foKZ40YyeOUh4lJMX3v4ONQSKaWY9OsRVn/yPiVvnKJ58jHWDW2Ig43007xfvy5tafPsQKwzUwjbsICpf5w2Op6YmMiCBQsAWLBgwUO7WuXFvcn6P/nkEwA++eQTmjZtarL88yslI4uXZi/FLewQNUo5MrXj47ECzj1paWnZ4gxzKZaBYVRUFKVKGTcnWFtb4+HhQVRUVK7yuHnzJrNmzcrW/Dxz5kx++ukntm3bRp8+fRg9ejRff/31I/Pz8/PD1dXVsM2ePTv3NyREHj0OAUBxldfmWq1Ww4yXOrP+px9xqN+Jm76BrDxxg5pzd/PH2WiTls3X15datWoBUKtWrWLXj1WvV7y+ej8bv5xKicRInCo1YPM3H8rgnQf4ZPLreFULJME9gA93XWHD6Tvff+YM3ubNm2dYwQlAr9cTHBzMvHnzCpx3QYxathPt4Z8oe3krc1uXeuw+M7NnzzaKMfz8/Mx2rSIVGE6aNCnHwR/3b+fOnSvwdXQ6Hd26daNGjRpMnz7d6NiUKVN46qmnqF+/Pu+88w4TJ07k448/fmSe4eHhxMfHG7bJkycXuJxCPEhxDwCKs/zW1lb0dmPPtx8wa+LruNhbExGfwuh3pvDsrGU59hErKgqr32JaZhb95m9h94LpOCTH4F6jKX+tnI+j/eM/T2F+aTQa1i+dR7VOL4BGy4DVxzgTlWDW4C0kJAQrK+Ogy8rKipCQkALnnV8/Hr3K4dVfYpWVTpPeg+nWtLbFymIukydPNooxwsPNN6CtSAWGb731FsHBwQ/dKlSogI+PDzdu3DA6NzMzk9jYWHx8fB56jYSEBDp37oyzszPr1q3DxsbmoembNGnCtWvXHtk07OLiYrTZ2ckfMyEeRwWprdVoNAxrUo7TE9rQwSsdz8h/Cft9Pk+9MJqfDl8qcNnM0XxYGN0W4lIy6PTVdi6u+h92qbfxbdieP5d+iZ3tw/8+C7CztWHNwAYEeDhiezWIZ+f8SPCFS2YL3gICAsjKyjLal5WVRUBAQIHzzo+QW8lM+d/HOCZGYR9Ql/mTX7VIOczNzs4uW5xhNibvtVgI7g0+OXr0qGHfX3/99cjBJ/Hx8app06aqdevWKikpKVfX+uCDD5S7u/tD80QGn4hCVlQ7fz8pTNGpX6/Xq/+t2aZqt+qkAgMDVZ1mrVXvmUtVbFJavvJLSEhQNWvWVFqtVgFKq9WqmjVrFvizERERoTZu3KgAtXHjRpMPdAqLTVa15u5SjN+gSj8zTr0y7TOl12cfSCEebsvBE4bBKP6dBxk+B/c2rVarZs+eXeDrmOtzlh/pmVmq0fj5KjAwUNVr0V6FRMYUehksRQaf/Ef16tXp3Lkzw4cP5/Dhw+zbt48xY8bQv39/Spe+M7/V9evXqVatGocPHwbuNB937NiRpKQklixZgk6nIyoqiqioKMOvn40bN/Ldd99x+vRpLl26xIIFC/jf//7H2LFjLXavQvxXUe38/aQwVa2cRqNhcr8OrP95DQ51O2CdnkT47/N56sXXWH8i781E5mo+dHZ2Zu/evQDs3bsXZ2fnAuV3v/0ht2g5dTmnoxKws9by1fS3WTz9TTQajcmukVvFfaqfTk3q0L7vS1hnpmCfEIlraX+02jtf8VqtlurVqzNmzJgCX8fJyYmDBw8yYcKd+QEnTJjAwYMHLbL2+/jfz5AQ9CdKo2X8e9Px9/Es9DI8lkweahaSW7duqRdeeEE5OTkpFxcXNXToUKNfLPd+0e/atUsppdSuXbuMfj3dv9371f/nn3+qevXqKScnJ1WiRAlVt25dtXDhQpWVlfXAckiNoShss2fPNlttgHg4c9WW3Kk93Kpqt+qkKnQaqHjzdzVwVZC6lYfawxEjRigbGxujz4WNjY0aMWJEvstlztqhBXsuqApdh6rAwEBVbuj/1D9XbuU5D1NO2/Q4TPWj1+tV72FjVWBgoPJv97xq13+42VoVzDUVTm4t3B+iGL9BaV//Sb325WqLlMGSzBl7FNvAsKiQwFAUNlMFADIXYt6ZOyi/FHVbdZ63UzF+g2L8BuU/4lO1bM/ZXDWtmqNs5sgzLSNLDVuyU1Vv3U0FBgaqBu26qX0n8je3oymDOXM3mReW5ORk9VTXZ1VgYKDyeXqQ2YI3SwaG289HKfvXf1SM36D6rTz6RHY9kKZkIYSBqTp/y1yIeWfuEZkVvd3YPLoNy/rVo1RaNB5BP/L5xJG0m/g1V24mPfTcMWPGUL16dZM2H5r6fq/cSqLV+8s4sngajolRlKjYgC2/rKZ5nfzNOWfKaZsel5H+Dg4OrPluPpoS7rjeugDA4bDbFi6V6Vy6mcTId2dT+chCAp0SWda/nkW6HjzOJDAUopgxVQAgcyHmnTlGZP63b1tUVBRDGvtxcOpzuDfsjHVmKgm7VtL1xZd5f+1e0jKzcszHHH2/THm/PwRdo9X4L8jcvgDrjGRqdurHrh8X4uWe/9GVpgzmzDUhtCWU9fVh9qzppFRtD8DQ+RvYfeKihUtVcHEpGTzz/nzcQv/BSmvFiiGtZOJzczB5HeQTRpqShSWYalSypfsJFTfm6HM3efJko+bQyZMnGx1ftvWQqvP0nabB+g0bq5ovTFC/BF19YPOZKd9TU9zv7eR09eL3QYrxG5T1mNWq+lNPqymfLjBJFwZT/TsoSiNtTeW/zeyuTXqqsNhkk+RtiWZ3XUqGajzxW1W/YSNVv1FT9dvfRx590mNM+hgWYRIYCksxRQAggWHemXKqoISEBFW1alWjgKRq1arZ8tQlp6mB0+epuk1bquqtuireXK/aL9ivTlzP/nfH1O9pfu9Xr9erlYeuqIoD3lMOwxcpxm9Qz604oiZOfs8k/QJNGcw9jgO67vUhXv3HDlWpfjNVo15DVXnCCpMEh4U9UCcpLUM99f5yVb9RE9WgYUM1Z8U6s16vOJA+hkIIk3ucms4Kk5OTE6NGjQJg1KhRBWqqnTdvHhcvXjSaYubixYvZpphxdrDl+2mvsWL1T1Ts/SpotOy4eJO278yjx6wVBEfdWTc1MjKS06fvrJl7+vRpk0y5kpCQQMuWLQFo2bIlCQkJjzzn/I1E2k7/no8mjMbt3BYqXN7MkufrsPalQMaNfc0kXRhMOT1PUVzNo6B8fX1p0KABL3Rtx7BXhuNgpXDc+y3t//czYbeTC5T3yJEjCQoKMmzm7IaSlplF7wW7SNq6CI0+k27DxjNxUG+zXU9IH0MhnkgyF2LRkNeApG6ADxvf7MGOV5tRt5Q9fhf/JPL3r3nmxcH0mbOGDz/7mh49egDQo0cPkwwoWrRoUa7zvB6fwojlu+n68hsk/vEVDskxOFdpxJrF83i5SXk0Go3J5kU0ZTBX1FbzMLVJo4fyzOCRWGem4rj3Wzr875cCBYf3gs57m7kG6qRn6um3MohtYamEV+pMuwGvMfPVF8xyLfH/rC1dACFE3v23ZsjOzi5Pf5wfVtsyadIks5TZUiIjI41qznx9fQv0RVbQZ3+//AYk7Sp7cuztDnxRAb5fuhinmCtc/fkTTth50XDIJAZ2bcNTAZ6UKVM6X+W638iRI+nZs6fhdU73GhGfykc7L/Hzr7/ie/4P3JUevZMnw8eOZ1SfjoZ0936QBAcHA3d+kPzxxx/5GiRjymBuzJgx/PDDD4Z/E6acELogTPnZfW/scADWrVh0t+YQ/nynD5U8S5ikrKYWl5JBry//YM8NLWg0TB/1EmNaPB6BepFn8sbpJ4z0MRSWUNA+PuaYDFmpojk34qMGd+SVKftXmaKfXEZmlvpw5UZVr2MfFRgYqAIDA1Wp599VVWbvULO3X1CXbybmu3wPk5mlV5vPRqneX/yhbN/epBi/QTmM+FbVe6qtGv3hfKVLyt6XzZR9+Uw9YKQoLjNpjr58H369WAUGBirfZ99SJd//U+25fLPgBTWxsNhkVeeNr1X9Rk1U6WfGqS/+vmzpIhU5MvikCJPAUFhCQQMwc3W2L2qrR+R2cEdemDr4vXjxoho06M5ExIMGDVIXL17MVz5ZWXq1eNMe9dQLo5XV2DV3Jsl+83fl32WIChz9kZq18ZgKCr+tMjIfvJLToySnZ6rt52+ocav3qyovTVE1WnZWDQIDlc1rP6gyM7aqeXuvqITk1Aeeb+ofJKYO5oraYCxzjf5ds2WP8p22RTF+g7J9e5P6/mi4SfI1hRPX41XlITNUg8CGqn7Dxmr6widvVZPcMGfsIU3JQhRDBW0ONVfT2ciRI2nYsCE9evRg48aNBAYGFii/gnrY4I78NpkX9Nn/1w8//MDKlSsBWLlyJQEBAUyfPj3P+Wi1Gl7p1pJXurXkQkwi3x0M49e/j+B+4xTcOMW6Q7+wytmXNPfy+FWuQdPGDanm5005NwfKuTtQyskOuButKcXtlAxCbiVzJTaZK7eS2Xf8LGFHdlLidggOSTdwvpvWtkw1PuscwPDOTbCzfviccqbuy3dvINDcuXMLPBDIlF0ETOW//THbtGljknz7dWrJU01S6L7kMJGHtzLpg60EvzKSmV2qY6W1zGTRSimWHw5l1twv8Li6D721HWPfncnQnu0tUp4nmUYppSxdiOJMp9Ph6upKfHw8Li75n6hViMKWmJjIrFmzmDt3LhMnTmTKlCkF+mK9JzQ0lICAAEJCQvD39y94QQtg5MiRLFu2jIyMDMM+Gxsbhg4dWmRWejF1H8j76fV61v1znB9+20joySNY6aIMx8IqdSWmTKM71wzdjX3KLRQaNNz5SrDKTEWh4XLtFwFwvn2FKie/R6FBW9KPag2a8OaQfjSo6p/r8tzfx/D+HyQFmYjbVJ+36dOnM2PGDMPradOm5StANxVzPKv/uhmfSLdez5KVGMvtktXw6fASK4e2oGIh9zuMS8lg+Iq9nFr7FU4J18iyd2Hup5/zdJO6hVqO4sScsYfUGArxhDJlbUtRVRxGm5q6BvJ+Wq2WPq0a0KdVAwAuXrvBb7sOcPDIMRw9quJi60h4XCquty5QIjH71DZZ1vZU8XQkoGQJqpUsS7n2FejfqQWlPd3zVZ57q7Pc+0EyYcKEAv0gMWUtX24G2RSmwhgg5unqxPo13/Pi8Ncg8hyJ62bT/HxvZg17huFNyxXKUnN7Lt9i0I//EnYrgWr6DBzL12LF13MIKO1t9muLnEmNYQFJjaEoriIjIwkKCjJq9i3ol6G5aiELUp4KFSoQExNj2Ofl5cWVK1cey0A4P/R6xcVrUYRG3ECj1aLRaNBqNJR0daKmfxlsbExbf2DKz11Rq+UzpcKs7U5PT+e9OV+w8/ef0aCI8alP5U4DmPtMPRqUdTPpte65GJPIhDX/sDvoLLqSlXGys+KTjuUZ3ur/l/sUD2bO2EMCwwKSwFAUV6b+Ui2Mpq/8uHTpEl9++SURERGULl2aN954g0qVKlmsPE86U37uzNkMb+nyfPTRR7z33nuGGkO4UwP84Ycfmm1Kqf1HgpgweQq6tCzOBo5EWdnwbG0fZnSqSi1f03y/RelSmb7xOJt/WY3ntUPorWxwem4aKwY3L/Qm7OJMAsMiTAJDUVyZ+kvVEl9kovgpasGcKZky6LXUD62UlBTWHT7HZ8cSCLoWj0f0Sayy0mnarhMvBJanew1vnO3zVoucnqln09lovtt5ghP/bMfr2iGsM1PQ25Wg6/MvMW30YGxsbMx0R48n6WMohDA5U38h31uJ4v7AMD8rUTzOgYN4vN9PU47Kd3JyYv369cyaNYuVK1cycODAQuma4eDgwIut6/NCK8W6k9f5cOyXaJLjCAvZweT11XjdpyYtmzehdWUfqpVyolopJ/zcHNDeHc2s1yviUzP497qOo+FxBF2LZ8fFGDLDTlPxzBp8AaW1pm7HZ/n0nTG4uUqFSlEjgaEQwiRMNdBj0aJFj22/MfF4M/X0Mqaayig/NBoNz9YtS6NVy5j5xUJOHNyLZ/RxPKOPc+X0Lxwq04iIgDtTyThl6HBIikKfmYHKzLiz9F5CJA5J0QQ3GA5aK2zdy2NXqjxt2nfgjUHP4e3lWSj3IfJOmpILSJqShbjDVE1fly5dMtSSDBo0iClTpkifQFHkmaPptyjVnmdkZLB3/0F+WL+ZM0cPkF6uAcFl25OWqaf0le34hu/Ldo7SWlF9yAxa1a/OM7V98Lo7V6YoOOljWIRJYCjE/yvoqOSiOoBFiEd5kvrYKqXIzMxEa2XN1dvJrNu0hRvXr2Jna4eDgz1OJRx4ql4tatWohq2traWL+1iSPoZCiGKhoHMjFsbcbUKYg6n62BYHGo3GMFikQskSvDW4j4VLJEyp2E4WFBsby4ABA3BxccHNzY1hw4aRmJj40HPatGmDRqMx2l599VWjNGFhYXTr1g1HR0dKlSrF22+/TWZmpjlvRQhx170v1/s9rl+u4vFSHCZTFyI3im1gOGDAAM6cOcO2bdvYtGkTe/bsYcSIEY88b/jw4YZ+G5GRkcydO9dwLCsri27dupGens7+/ftZsWIFy5cvZ+rUqea8FSHEXfLlKoqrMWPGUL36/0/ObKr1x4UobMUyMAwODmbLli189913NGnShBYtWvD111+zZs0aIiIiHnquo6MjPj4+hu3+tvmtW7dy9uxZfvjhB+rVq0eXLl2YNWsW8+fPJz093dy3JUSx998lyu7vOJ8b8uUqiqt7y/1NmDABgAkTJkjfWFEsFcvA8MCBA7i5udGwYUPDvg4dOqDVajl06NBDz121ahWenp7UqlWLyZMnk5ycbJRv7dq18fb+/zUaO3XqhE6n48yZMw/NV6fTGW1paWn5vDshiq9FixbRo0cPAHr06JHnpbvuzd02cOBAAAYOHMj69evly1UUCwkJCbRs2RKAli1bkpCQYOESicdFWlpatjjDXIplYBgVFUWpUqWM9llbW+Ph4UFUVNQDz3vxxRf54Ycf2LVrF5MnT+b77783fAHdy/f+oBAwvH5YvgB+fn64uroattmzZ+f1toQo9kaOHElQUJBhGzlyZJ7z+O/cbT/88IOpiymEWRT0h5EQDzJ79myjGMPPz89s1ypSo5InTZrEnDlzHpomODg43/nf3wexdu3a+Pr60r59ey5fvkzFihXznS9AeHi4UbO0nZ3M1ySePKaYZ23kyJH07NnTKE8higP57ApzmTx5MuPHjze81ul0ZgsOi1Rg+NZbbzFkyJCHpqlQoQI+Pj7cuHHDaH9mZiaxsbH4+Pjk+npNmjQB7kyoW7FiRXx8fDh8+LBRmujoaIBH5uvi4iLzGAphAo/zkmni8SafXWEudnZ2hVbhVKQCQy8vL7y8vB6ZrlmzZsTFxREUFGRYi3Lnzp3o9XpDsJcbx48fB/7/V12zZs348MMPuXHjhqGpetu2bbi4uFCjRo083o0QQgghRPFSLPsYVq9enc6dOzN8+HAOHz7Mvn37GDNmDP3796d06dIAXL9+nWrVqhlqAC9fvsysWbMICgoiNDSUDRs2MGjQIFq1akWdOnUA6NixIzVq1OCll17ixIkT/PXXX7z//vu89tpr0jQshBBCiMdesQwM4c7o4mrVqtG+fXu6du1KixYt+Pbbbw3HMzIyOH/+vGHUsa2tLdu3b6djx45Uq1aNt956iz59+rBx40bDOVZWVmzatAkrKyuaNWvGwIEDGTRoEDNnziz0+3vcTZ8+nd69e+f7/N69e+drMfndu3fj5uaW7+uaU2hoKBqNhri4OEsXRQghxBOqSDUl54WHhwerV69+4HF/f3/uXwbaz8+Pv//++5H5li9fns2bN5ukjEIIIYQQxUmxrTEUQgghhBCmJYHhE06n0zFmzBjKly+Pi4sLjRo1Ijw8HLgzIvv555/Hy8uLcuXK8d577xnWjV6+fDn16tUzyqtevXosX77c6Pi7775LyZIlKVeuHN98880Dy3Hjxg0GDBiAr68vpUuXZty4cUaThP/6669UqlQJV1dXhg8f/sj1q4OCgmjXrh0eHh54eXkxduxYo+Pfffcdfn5+lCxZkokTJxr2h4WF8fTTT+Pl5YW7uzvdunUjNDTUcHzIkCEMHz6c/v374+zsTNWqVdm9e7fheJs2bZg8eTKdOnXC2dmZBg0acOrUKcPxxMRExowZQ7ly5ShVqhSDBg0iPj4+x3vYtm0bderUwdnZGW9vb0aNGvXQexZCCCEKSgLDQpKZpedaXEqhbZlZ+lyVa8iQIVy6dIkDBw4QFxfHt99+i4ODA3BnQnAbGxtCQkLYu3cv69evN1pb+lFOnz6NRqMhMjKStWvXMmnSJPbs2ZMtnVKKnj174uPjw+XLlzl16hQnTpzggw8+AODChQu8+OKLfP7559y6dYvAwEC2bNnywOtev36ddu3a0bdvXyIiIrh69SrPP/+84XhCQgJnz57l4sWL/PPPP8yfP98Q3On1esaPH094eDhXr17F0dGR4cOHG+W/du1aXn31VeLi4njppZeyTbH0/fffM3fuXG7fvk3Dhg2NgtKXX36Z2NhYTp48SUhICBkZGQ9c7m3w4MG8/fbbJCQkcOXKFV566aWHPm8hhBCioIptH8PiJiohDb9Z2wvteuFTOlDWzeGhaaKjo1m3bh1Xr141jOauX78+cCe42rlzJ1FRUTg5OeHk5MR7773H9OnTeffdd3NVhhIlSjB9+nRsbGxo1qwZAwYMYOXKlbRq1coo3dGjR7l48SL79+9Hq9Xi6OjIu+++y6uvvsqsWbNYu3Yt7du3N6wo8Oqrr/Lll18+8Lo//PADgYGBjB492rDv3jJVcCcQ/eCDD7C3t6d69eo0b96coKAg2rRpg7+/P/7+/gDY29vz3nvv0bRpU/R6vWH93q5du9KmTRsAhg4dypQpU7h16xYlS5YE7izjVrduXeBOcNe5c2cAYmJi+PXXX7l586ZhAMzMmTOpWbOmoab1fjY2Nly6dImYmBi8vLxo3rx5rp67EEIIkV9SY/gEu3r1KnZ2dpQrVy7bsWvXrmFvb2+0RGCFChW4du1arvMvXbo0NjY2htfly5fn+vXr2dKFhoYSFxeHh4cHbm5uuLm50bdvX8Pk4hEREZQvX97onP++/u99Va5c+YHHXVxccHR0NLwuUaKEYU3TmJgYXnzxRfz8/HBxcaFVq1akpaUZrXl6/2TnJUqUAHjo8cTERMN96vV6AgICDPfZqFEjtFptjksurlu3jtOnT1O1alXq16/PTz/99MB7EkIIIUxBagwLiY+zHeFTOhTq9R6lfPnypKWlER4enm1pnbJly5Kamkp0dLQhOAwNDaVs2bIAODk5GaYCuue/wU1ERAQZGRmG4DAsLIwyZcpkK4efnx+lSpUiMjIyx3KWLl2aAwcOGO0LCwujadOmD7yvrVu3Pui2H2ry5MkkJydz7NgxvLy8OH78OPXr1zca4Z5ffn5+aLVaIiIijALTe+7vywjQoEEDfv31V/R6PevXr+f555+ndevW2dbzFkIIIUxFagwLibWVlrJuDoW2WVs9+q319vamV69evPrqq0RGRqLX6/n333+5desWZcqUoW3btkyYMIGkpCTCwsL48MMPGTx4MHBnoMmVK1fYu3cvmZmZzJ07l1u3bhnln5SUxKxZs0hPT+fQoUOsWrWKAQMGZCtHo0aN8PPz4/333ychIQGlFFevXuXPP/8E4Pnnn2fHjh388ccfZGZmsnjxYi5cuPDA+xowYACHDx9m4cKFpKWlkZyczN69e3P1Pul0OhwdHXFzc+PWrVvMmDEjV+flho+PD71792bMmDHcvHkTuBNMr1u3Llva9PR0vv/+e27fvo1WqzU0PVtby285IYQQ5iOB4RNuxYoV+Pn50bBhQ9zc3Hj11VdJSUkBYPXq1aSkpFC+fHmeeuopunXrZhjBW6lSJebOnUvfvn3x9fUlLS2NmjVrGuVdq1YtMjMz8fX1pW/fvnz44Ye0bds2WxnuTSx+/fp1qlevjqurK926dePSpUsAVK1ale+//57XX3+dkiVLcujQIUO/vZyULVuWHTt2sHr1ary9vfH39+eXX37J1fOYMWMGly5dwt3dnaeeeoouXbrk6rzcWr58uaEJ2cXFhZYtWxIUFJRj2tWrV1OpUiWcnZ0ZO3Ysq1evNvRjFEIIIcxBo0zRRvYE0+l0uLq6Eh8fj4uLi6WLU2QsX76cL774wrAetRBCCCFMw5yxh9QYCiGEEEIIQAJDIYQQQghxlzQlF5A0JQshhBCiMElTshBCCCGEMDsJDIUQQgghBCCBoRBCCCGEuEsCQyGEEEIIAUhgKIQQQggh7pLAUAghhBBCABIYCiGEEEKIuyQwFLRp0wY7OzucnZ1xdXWlVq1avPXWW8TExAB31jxeuHChIX1ycjJ2dnYMGTLEKJ+GDRvy6aefGuXp5OSEs7MzNWvW5Oeff8527aSkJFxcXGjSpIn5blAIIYQQuVJsA8PY2FgGDBiAi4sLbm5uDBs2jMTExAemDw0NRaPR5LjdH7DkdHzNmjWFcUsWNWfOHBISEoiLi+Onn37i+vXrBAYGEh0dTdu2bdm9e7ch7b59+6hYsaLRvvj4eP7991/atWtnlGdiYiI6nY65c+cyYMAArl69anTdn376CSsrK44cOcLp06fNfZtCCCGEeIhiGxgOGDCAM2fOsG3bNjZt2sSePXsYMWLEA9P7+fkRGRlptM2YMQMnJye6dOlilHbZsmVG6Xr37m3muyk6NBoNNWrU4IcffsDFxYVPP/00W2C4e/duXnzxRaysrAgJCQFgz549uLq6Urdu3Rzz7NatG25ubpw/f97o2JIlSxg6dCitWrViyZIlZr03IYQQQjyctaULkB/BwcFs2bKFI0eO0LBhQwC+/vprunbtyieffELp0qWznWNlZYWPj4/RvnXr1vH888/j5ORktN/NzS1bWlPp1KlTjvubN2/OtGnTAFi/fj0LFizIMd3cuXMNwdfQoUOJiIjIluavv/4qcDmtra3p3bs327Zt45133uHGjRsEBwdTvXp1du/ezZw5c7hy5Qq7d+8mICCA3bt307p1a7Ta7L819Ho9GzduJCUlhXr16hn2nz9/nn379vHNN99Qu3ZtJk6cyJw5c7C1tS1w+YUQQgiRd8WyxvDAgQO4ubkZgkKADh06oNVqOXToUK7yCAoK4vjx4wwbNizbsddeew1PT08aN27M0qVLyc1y0jqdzmhLS0vL/Q0VUWXKlCE2NpaSJUtSp04ddu3aRXJyMmfOnKFx48a0bt2aXbt2AXdqEe9vRgaYPHkybm5ulChRgmeffZb333+fUqVKGY4vWbKEevXqUadOHfr27UtycjK///57od6jEEIIUdSlpaVlizPMpVjWGEZFRRkFGHCnhsvDw4OoqKhc5bFkyRKqV69O8+bNjfbPnDmTdu3a4ejoyNatWxk9ejSJiYm8/vrrD83Pz8/P6PW0adOYPn16tnS5qc3r3bt3rpqvly1b9sg0BXH9+nU8PDwAaNu2Lbt27aJy5coEBgZia2tL69atmTJlCvHx8Rw/fpy2bdsanT979mzGjRsHwKVLl+jZsydubm6MHDmSzMxMVq5cyaRJkwBwdnbmmWeeYcmSJTz33HNmvS8hhBCiOJk9ezYzZswolGsVqcBw0qRJzJkz56FpgoODC3ydlJQUVq9ezZQpU7Idu39f/fr1SUpK4uOPP35kYBgeHo6Li4vhtZ2dXYHLaUmZmZn8/vvvdO3aFbgTGL7yyitUrlyZ1q1bA+Dv749Go2HZsmV4enpSs2bNB+ZXqVIlunbtyqZNmxg5ciSbNm0iOjqaWbNm8dFHHwF3RjsnJSURHh6eLdAWQgghnlSTJ09m/Pjxhtc6nc5s35NFKjB86623sk2B8l8VKlTAx8eHGzduGO3PzMwkNjY2V30Df/nlF5KTkxk0aNAj0zZp0oRZs2aRlpb20GDPxcXFKDAszs6dO8esWbOIj483fBBbtWrFrVu3WLZsGWvXrjWkbd26NXPmzKFNmzZoNJoH5hkaGsrmzZsNNaFLliyhZ8+eLFq0yChd69atWbZsGVOnTjX9jQkhhBDFkJ2dXeFVOKli6OzZswpQR48eNez766+/lEajUdevX3/k+a1bt1Z9+vTJ1bU++OAD5e7u/sDj8fHxClDx8fG5yq8oat26tbK1tVVOTk7KxcVFVa9eXY0fP15FR0cbpQsMDFT29vYqNTXVsO+7775TgFqwYEGOeZYoUUKVKFFClSlTRo0dO1alpKSo69evKysrK7V79+5sZfn666+Vv7+/0uv15rlZIYQQopgzZ+yhUSoXIyuKoC5duhAdHc3ChQvJyMhg6NChNGzYkNWrVwN3+se1b9+elStX0rhxY8N5ly5dokqVKmzevJnOnTsb5blx40aio6Np2rQp9vb2bNu2jQkTJjBhwoQHtu3rdDpcXV2Jj49/bGoMhRBCCFF0mTP2KFJNyXmxatUqxowZQ/v27dFqtfTp04evvvrKcDwjI4Pz58+TnJxsdN7SpUspW7YsHTt2zJanjY0N8+fP580330QpRaVKlfjss88YPny42e9HCCGEEMLSim2NYVEhNYZCCCGEKEzmjD2K5TyGQgghhBDC9CQwFEIIIYQQgASGQgghhBDiLgkMhRBCCCEEUIxHJT8JIiMjiYyMNLz29fXF19fXgiUSQgghxONMagyLsEWLFhEYGGjY/rtKiBBCCCGEKUlgWISNHDmSjRs3Ancm3x45cqSFS1T0rFmzhueff97SxQBg3759tGjRwtLFEEIIIfJNAsMizNfXl1q1agFQq1YtszUj//PPP3Tt2hUPDw9cXFyoUqUKY8eOJTQ01CzXy402bdrwxRdfPDSNXq/n3XffZcqUKYZ9U6ZMoXbt2lhbWzNu3Lhs52zbto0GDRrg7OxMjRo12LJli+HYqlWrcHJyMto0Gg2fffaZIU1aWhoTJkzA19cXJycnateubXhOTz31FDY2Nvz+++8FunchhBDCUiQwLMISExNZsGABAAsWLCAxMdHk19i4cSNdunShY8eOnDt3Dp1Ox99//02FChXYtWuXya9nSps3b8bDw4PatWsb9lWqVIm5c+fSs2fPbOmvXLnCM888w8yZM4mPj2fu3Ln06dOHK1euADBgwAASExMN299//41Wq+W5554z5DF06FAuX75MUFAQCQkJ/Pzzz7i5uRmODx48mHnz5pnvpoUQQghzMvnqy08Ycy1knZCQoGrWrKm0Wq0ClFarVTVr1lQJCQkmu4Zer1f+/v7qf//73yPTHjlyRDVv3ly5urqq6tWrq9WrVxuOBQUFqSZNmihnZ2dVsmRJ1b17d0P+EydOVN7e3srZ2VlVrlxZbdy40XDejz/+qGrXrq1cXV1Vw4YN1b59+5RSSo0fP15ptVpla2urSpQooTp37pxjmYYPH67efvvtHI8NHjxYvfHGG0b75s+fr1q2bGm0r02bNmratGk55jFq1Cija58+fVo5Ojqq2NjYnB+SUiosLExZW1srnU73wDRCCCFEQZgr9lBKKakxLKLmzZtHcHAwer0euNNsGhwcbNLaqAsXLhAaGkq/fv0emi4uLo7OnTvTv39/YmJiWLBgAcOHD2ffvn0AjBkzhh49ehAXF8f169d5++23gTvNtqtXr+bYsWPodDq2b99OlSpVgDu1fRMmTGD58uXExsYyefJkevTowa1bt/j0009p2bIlc+bMITExkT///DPHch0/fpxq1arl+n71ej3qPytA6vV6Tp48mS1tSkoKq1ev5pVXXjHs+/vvv/H39+f999/Hy8uLypUrM3fuXKPz/Pz8sLe35/Tp07kulxBCCFFUSGBYRIWEhGBlZWW0z8rKipCQEJNd4+bNmwCULl3asG/GjBm4ubnh5ORkGNTxxx9/4OXlxdixY7GxsaF169a8+OKLrFixAgAbGxuuXr1KREQEdnZ2tGrVyrA/NTWVM2fOkJGRQbly5QyB4fz583n77bdp0KABWq2WZ599lmrVqrF58+Zcl//27dt5WiPy6aef5siRI6xfv57MzEzWr1/Pvn370Ol02dL+8ssv2NraGjVJx8bGcvbsWZycnAgPD2f9+vV8+eWXfP/990bnuri4cPv27VyXSwghhCgqJDAsogICAsjKyjLal5WVRUBAgMmu4enpCUBERIRh37Rp04iLi2PChAmkp6cDcO3aNfz9/Y3OrVChAteuXQNg6dKlpKamEhgYSLVq1Qy1mm3btmXGjBlMmTIFT09P+vTpYwhsQ0NDeffdd3FzczNsx48f5/r167kuv7u7e45B3YNUrVqVtWvXMmPGDEqVKsWSJUvo378/JUuWzJZ2yZIlDBo0CBsbG8M+JycnrKysmDlzJvb29tSsWZOXX37ZMHL8Hp1Oh7u7e67LJYQQQhQVEhgWUWPGjKF69epotXfeIq1WS/Xq1RkzZozJrlGlShXKly/PTz/99NB0ZcuWzTZCOTQ0lLJlywJQsWJFVq5cSVRUFN999x0TJkwgKCgIgNGjR3Pw4EHCwsKws7Pj9ddfB+40uX766afExcUZtqSkJCZNmmS430epV68e586dy9M99+rVi3///ZfY2Fg2btzIxYsXad26tVGaS5cusWfPHqNmZIC6desCoNFoHph/eHg4qamphtHkQgghRHEigWER5eTkxMGDB5kwYQIAEyZM4ODBgzg5OZnsGhqNhi+//JIPP/yQr776ihs3bgAQExPDmTNnDOm6du3KjRs3+Oabb8jMzGTv3r2sWrWKQYMGAbBy5Uqio6PRaDS4ubmh1WqxsrLiyJEj7N+/n/T0dBwcHChRogTW1ncW23nttdf4+OOPCQoKQilFcnIy27dvN9RCent7c/ny5YeWv0ePHtlGTmdkZJCamkpWVhZZWVmkpqaSkZFhOH706FEyMzNJSEhg5syZxMbGMnjwYKM8lixZQrNmzbL1X2zVqhWVK1dmxowZZGRkcP78eZYvX06vXr0MaXbu3EmrVq1wdnbO1XsghBBCFCkmH87yhDHnyCCllAoJCVGACgkJMUv+Sin1999/q06dOilXV1fl7OysqlatqkaPHq2uXLliSHPo0CHVrFkz5eLioqpVq6a+//57w7GXXnpJeXt7qxIlSqgKFSqoefPmKaWU2r59u6pbt65ycnJS7u7uqmvXrurq1auG83766SdVv3595erqqkqVKqW6d+9uOH7w4EFVrVo15erqqrp165ZjuTMzM5W/v786deqUYd/gwYMVYLQNHjzYcLxDhw7K2dlZubi4qD59+qjw8PBsefr6+qqlS5fmeM0LFy6otm3bKkdHR+Xv768+/vhjo+Pt2rVTv/3228MetxBCCFEg5ow9NEr9Z5imyBOdToerqyvx8fF5GgiRG5GRkQQFBdGjRw82btxIYGCgrJX8Hz/++CPr169n7dq1li4K+/fvZ+LEifzzzz+WLooQQojHmDljDwkMC8icb8706dOZMWOG4fW0adOYPn26Sa8hhBBCiOLFnLGHtUlzEyY1cuRIo+lSpLZQCCGEEOZUbAeffPjhhzRv3hxHR0ejJckeRinF1KlT8fX1xcHBgQ4dOnDx4kWjNLGxsQwYMAAXFxfc3NwYNmyYWZaiyw1fX18aNGhg2CQwFEIIIYQ5FdvAMD09neeee45Ro0bl+py5c+fy1VdfsXDhQg4dOkSJEiXo1KkTqamphjQDBgzgzJkzbNu2jU2bNrFnzx5GjBhhjlsQQgghhChSin0fw+XLlzNu3Dji4uIemk4pRenSpXnrrbcMU8DEx8fj7e3N8uXL6d+/P8HBwdSoUYMjR47QsGFDALZs2ULXrl25du2a0Qoh95iznV8IIYQQ4r/MGXsU2xrDvAoJCSEqKooOHToY9rm6utKkSRMOHDgAwIEDB3BzczMEhQAdOnRAq9Vy6NChQi+zEEIIIURhemIGn0RFRQF3Jk6+n7e3t+FYVFQUpUqVMjpubW2Nh4eHIc2D/HdpNjs7O+zs7ApabCGEEEI84dLS0khLSzO8zstysHlVpGoMJ02ahEajeeiW1yXQCoufnx+urq6Gbfbs2ZYukhBCCCEeA7NnzzaKMfz8/Mx2rSIVGL711lsEBwc/dKtQoUK+8vbx8QEgOjraaH90dLThmI+Pj2FZuHsyMzOJjY01pPmvexH85cuXiY+PN2yTJ0/OVzmfFGlpaUyfPt3oF5B4OHlm+SPPLe/kmeWPPLe8k2eWO5MnTzaKMe4tGWuO5/bEDT6ZMGECb731FnCnKrZUqVLZBp8cPXqUwMBAALZu3Urnzp0fOPjk2rVr+Pn5ER4eTtmyZU1+f48rGbSTd/LM8keeW97JM8sfeW55J88sf8wZexSpGsO8CAsL4/jx44SFhZGVlcXx48c5fvy40ZyD1apVY926dQBoNBrGjRvHBx98wIYNGzh16hSDBg2idOnS9O7dG4Dq1avTuXNnhg8fzuHDh9m3bx9jxoyhf//+OQaFQgghhBCPk2I7+GTq1KmsWLHC8Lp+/foA7Nq1izZt2gBw/vx54uPjDWkmTpxIUlISI0aMIC4ujhYtWrBlyxbs7e0NaVatWsWYMWNo3749Wq2WPn368NVXXxXOTQkhhBBCWFCxDQyXL1/O8uXLH5rmv63kGo2GmTNnMnPmzAee4+HhwerVq3NdjnvXSEhIMOsoocfNvWclzyz35Jnljzy3vJNnlj/y3PJOnln+JCQkANnjHFMo9n0MLe3KlStUrFjR0sUQQgghxBPm8uXL+R6U+yASGBaQXq8nIiICZ2dnNBqNpYsjhBBCiMecUoqEhARKly6NVmva4SISGAohhBBCCKAYj0oWQgghhBCmJYGhEEIIIYQAJDAUQgghhBB3SWAohBBCCCEACQyFEEIIIcRdEhgKIYQQQghAAkMhhBBCCHGXBIZCCCGEEAKQwFAIIYQQQtwlgaEQQgghhAAkMBRCCCGEEHdJYCiEEEIIIQAJDIUQQgghxF0SGAohhBBCCEACQyGEEEIIcZcEhkIIIYQQAgBrSxeguNPr9URERODs7IxGo7F0cYQQQgjxmFNKkZCQQOnSpdFqTVvHJ4FhAYWGhlKxYkVLF0MIIYQQT5jLly9ToUIFk+YpgWEB2djYAHD27FnKlClj4dIUHzqdDj8/P8LDw3FxcbF0cYoFeWb5I88t7+SZ5Y88t7yTZ5Y/169fp0aNGoYYxJQkMCyge83Hzs7O8qHOBxcXF3lueSTPLH/kueWdPLP8keeWd/LM8kan0wGYpQubDD4RQgghhBCABIZCCCGEEOIuCQwLyM7Ozui/Infs7OyYNm2aPLc8kGeWP/Lc8k6eWf7Ic8s7eWb5Y87YQ6OUUibP9Qmi0+lwdXUlPj5e+kcIIYQQwuzMGXtIjaEQQgghhAAkMBRCCCGEEHdJYCiEEEIIIQAJDIUQQgghxF0ywbUJZWbpuXQzie8+n42HmwtuLi44Ozvj4eGBt7c3ZcuWxcfHx9LFFEIIIYTIkQSGJhShS6Xm//6i/r6tOR73qN6YVoPGUaGkI8lXThB2JojKFQKoXLky1apVw83NrXALLIQQQghxHwkMTeh2SgZ6KxvONByFVWYq1pmpWGWmYJOeiE1aAiF6X7ZtOQ9A2Ut/4n39sNH5zu4lqVK1GtPfm4Svr68lbkEIIYQQTzCZx7CA7p9LqISTM3EpGdxOySDu7nY7JYMoXRrX4lO4Hp9KeFwKl24mE33rNvbJN7FPvoljUhQOCVE4JkWhzcogvONUmlfypmkZJ06s/pQ2zZvw1FPNqV69OlZWVpa+ZSGEEEJYkDnnMZTAsIDy++YkpGZy8WYiwdGJHI/Q8e/1eP69dpvE2JukO7gDUCI+nGrHlxrOsXN0IrBRY7o+3Y4WLVrg5ORk8vsRQgghRNEmgWERNH/+fObPn09WVhYXLlwwyZujlCI0NoV9obH8ExLL3iu3uHj1Oi63r+By+zIuty9jnZkKQO1eLzNu2ADq+LqQlZWFtbX0ChBCCCGeBBIYFmHmXhIvOiGN7Rdi2Hohhq3nokm4fhn3m8FE+T1Fpq0TFT3sKbPnS6pVqcTzvXvQokULWXNSCCGEeIxJYFiEFeZayUopgq7Fs+5UJOtORxEcnYhNajxVjy/DLi0eAFuHEnTp2pUXnutDpUqVzFoeIYQQQhQ+CQyLsMIMDP/rXHQCa49HsCoonMjL5/C4cRKPG2ewykoDoNfgUbz32stotZpCLZcQQgghzEcCwyLMkoHhPfdqEn8IusaqQ1dQYSfwjDxGSLXelPfzY2yLALyij9GhTWs8PT0tUkYhhBBCmIYEhkVYUQgM75eWmcW6U1EsPhjGzks3AbBPjKZm0EI0WitatXua10a8TIUKFSxcUiGEEELkhwSGRVhRCwzvdzEmkfn7Qlm+7wL2oYcpFXEY2zQdAPWaNOeNV4dTu3ZtC5dSCCGEEHkhgWERVpQDw3sSUjNZfiScL/++wO3gw/iE78Mh+U5t4rgPvmBg5xYWLqEQQgghcksCwyKsOASG92TpFb+dimT29vNcOXkU19iLhFXuTocqXrzToizeKk5qEIUQQogiTgLDIqw4BYb3KKXYfuEmH2y/wJ4rsQD4hu6m9NW/qdekBVMmvkn58uUtXEohhBBC5MScsYfWpLmJYkGj0fB0VS92j27Ojleb0SLAgySXsqSUKMXxQ//Q57nnmDprNnFxcZYuqhBCCCEKkdQYFlBxrDH8L6UUOy7eZPKms4Qc3U2Z0F3YpCdiZefAqFGjGDLwRUsXUQghhBB3SY2hMCuNRkOHKl4cGteKrye8QnzHt4ko35r0jExmbT7FV3uvkJ6pt3QxhRBCCGFmUmNYQI9DjeF/pWZkMe+fUD764xixmVYoKxsqlXSkTewuxg19gZo1a1q6iEIIIcQTSwafFGGPY2B4z62kdD7YfoH5+0Kxu3mFqidXAho69ujFu2+Nw8nJydJFFEIIIZ440pQsLKJkCVs+71WL4Ilt6dSqGZdq9iPdzpmtG9fTqXtvNv6xGfldIYQQQjw+pMawgB7nGsP/2nY+htE/HSUlaDPe1w6iQVGpVn2+X/wNNjY2li6eEEII8USQGkNRJDxd1YtTkzoyfNQYLjV+lUSXshyK0TPwx5NExKdaunhCCCGEKCCpMSygJ6nG8H4XYxJ57deT7DgXid7KFmc7a16wPc+o3m2pV6+upYsnhBBCPLakxlAUOZW9nPhrZDN+HNIMXxc70m9HcXTTD7wy/BWmz/6E1FSpQRRCCCGKGwkMxf+1d9/hUVX5H8ffk0x6JQkkJIQiHUINECOIIG3pAiIdFBVROoJiQ3fVxYo1llUUdS3orgUsKL2bQEhAaighlCQECOl95v7+UPMTQVcgk8kkn9fzzANz78093zli8sk59557xUwmE7e0D+XA/T25o08njrQeTYmLF1//9xMGDB/Frl277V2iiIiIXAZNJV+lmjqVfCkbjpzl9g+2Ydn+BYGnd4PJxOCbR/Pg3Jm6OUVERKSCaCpZHMINjYPY9UB/Bt0+h8OtR1Pq4sWy7zfx6pYUrFb9/iEiIlLVacTwKmnE8NJ+HT08cSaLEnd/ujUK4JFID27s3Baz2Wzv8kRERByWRgzF4fw6ejilV3sA4vYd5v7Z0xkyajwpKSn2LU5EREQuScFQbMbLzcwrw9uw/p5owoL8yPVrSEbKYW4ePZYPP/2vnpoiIiJSxSgYXqGYmBhatWpF586d7V1KlXdD4yASHhhI9KR5pDQdgMVi4YVnFnHrPbPJysqyd3kiIiLyC11jeJV0jeHl+SThFDOWrqb2rs/wzEvHu25DVn7+Ce4uuu5QRETkr9A1hlJtjO4QRvxjIwkaPp/0etEkBHUl6qUt7E3PtXdpIiIiNZ6CoVS6+rU8WTe9O9NnzKQgqCm703Lo8uwqRt01mxMnTti7PBERkRpLwVDswtnJxIJeTdk2sxvNanvhdXInR+I3M3zUGJZ/+729yxMREamRFAzFrjqF+7NzTncG3DSCE437YSkt4R8LH2Luw/+guLjY3uWJiIjUKAqGYndebmaWjunAs/OncrzznRS7+7Nx5XIG3aI1D0VERCqTgqFUGRM7hbP50dGYBtzL+aCWnD+VzG3PfUBmQYm9SxMREakRFAylSmkR7EPs/L70vn0eh1uPYp1LW9o/v4EtR89pallERMTGFAylyvFwcebNke14c/ZYfNxdOJFVxIiHXmLAiNEcOXLU3uWJiIhUWwqGUmWN6hDGzrnd6VjPD/ecU2Snn2DUuPF89J8v7F2aiIhItaRgKFVakyAvts7oypA7ZpPSdBBWq5XFTz3J1HsXUFhYaO/yREREqhUFQ6ny3MzOvDysDW89eBenoqdS5BHIjg2rGTBiNCnHtSC2iIhIRVEwFIdxU5u6xD56C15D53OuTlvO5uQz+YskTufqphQREZGKYDIMw7B3EY7Mlg+ylksrKbPywDf7eHn1HspcvQjxcePFnkEM6dISDw8Pe5cnIiJiU7bMHhoxFIfjanbi+aERfD61B7U8XDiTeZ4nH5xH/xGjOXTosL3LExERcVgKhuKwBrcOIfHe7nRqEESuf0PyMk4xZvwE/v3p52ggXERE5PIpGIpDq1/Lk02zezLirns51nwIVsPgxWf+yV1zF1BQUGDv8kRERByKgqE4PBdnJ54b0pqlD91F6nV3U+gZxM5Naxg8eiIlpWX2Lk9ERMRhKBhKtTGoVTBxj47Ed+h8zgW3Y7dXBP3f3k56TpG9SxMREXEIuiv5Kumu5Kqn1GLl4e8O8Mzaw2AyEeztwhTfZBbcPQlPT097lyciInJVdFeyyGVwcXbi6UGt+PqOKAI8XbAe3My3Hy9hwIjRHEw6ZO/yREREqiwFQ6m2BrYKJnHuDTTp0pNzwe3IO5PKuAkTef+T/+iuZRERkUtQMJRqLbyWBxtm9WTU3fM41nwoVsPg5eee4vaZ88jNzbV3eSIiIlWKgqFUey7OTjw1qCXvPzyFtOvuptCzNru3bWDiw89TVGqxd3kiIiJVhoKh1BgDWgaz/bFbCLp5Aaca9uQrU1uiXtrM3vRcLBYFRBEREQVDqVHq+Xuwdnp3Zt9zF84uruxOy6HHQ2/Rf8RokpOT7V2eiIiIXSkYSo3j7GTi/hub8OPMbjSv7YXn6f1knkxm5JixvPPvT3RjioiI1FgKhlJjRYb7Ez+nO/0nTSel2SAsVnjtxecYd+c0zp49a+/yREREKp2CodRoXm5m3hjZjiUPTiW923TyfUJJSoxjwNDhrN6w2d7liYiIVCoFQxFgSEQICY+NoMGo+znVsCclFoNbv03lqz3p9i5NRESk0uiReFdJj8SrXgzD4J24E8z7fCdZZc4A3BQOgwNzuXXcaJyc9LuUiIjYlx6JJ1JJTCYTt0fVZ8+D/RjcKhiAvV+/z2svLWb4uFs5duyYfQsUERGxIQVDkUsI8/Pgq8md+WhcR3LaDSPHvxEnD+3j5lFjeOWNtyktLbV3iSIiIhVOwVDkD5hMJsZ0DGP3YyNoP+H+n+9cxon33n6Dvw29mYTERHuXKCIiUqEUDEX+hzo+bnw6qRP/euAuMnrOIbNOBNkZp7jtk0S2JGfauzwREZEKo5tPrpJuPqlZsgpLWbjyIG//sJ1CzyAAxrX0pV3+T9xzx214eXnZuUIREanudPOJHXz99dc0b96cpk2b8vbbb9u7HKki/D1ceHlYBFsfHk7XhrUA2LDiU5Z9sJQ+Awbz0bLPKCsrs3OVIiIiV0YjhpdQVlZGq1atWLduHX5+fkRGRrJ161YCAwMvOlYjhjWX1Wrw4c6TPPjlTvhpFbVT43AyrPgHhzJn2t38rV9fnJ2d7V2miIhUMxoxrGRxcXG0bt2asLAwvL296d+/Pz/88IO9y5IqxsnJxIRO4RxcOJAp02eSfN1MMmu3Jut0Ko8ufIRbJt+Nxarfu0RExHFUy2C4ceNGBg8eTGhoKCaTiS+//PKiY2JiYmjYsCHu7u5ERUURFxdXvi81NZWwsLDy92FhYZw6daoyShcH5Olq5pE+zdj3+M30mTyXg52ncj6wBRtLQ2nz3Ho+3nmKo8nJWuJGRESqPLMtTrp8+fLL/po+ffrg4eFRIe3n5+fTrl07Jk+ezPDhwy/av2zZMubOncsbb7xBVFQUL774Iv369ePgwYPUqVOnQmqQmqeurztvjmzHfT2b8I9Vkfx7xwnOns5j7L930D4+Bl8XE7dOHM/I4cPw9va2d7kiIiIXsck1hpf72DCTycShQ4e45pprKroUTCYTX3zxBTfddFP5tqioKDp37syrr74KgNVqJTw8nBkzZrBgwQK2bt3Ks88+yxdffAHA7Nmz6dKlC2PHjr3o/L/O8584ceKCeX43Nzfc3Nwq/POI4zhwOpfHVx3is+1HCD38PQEZu3EyrDi7utGn39+YOGYUzZo1s3eZIiJSxRUXF1NcXFz+Picnh/DwcMe6xjA9PR2r1fqXXp6enrYq4yIlJSXEx8fTu3fv8m1OTk707t2bbdu2AdClSxf27NnDqVOnyMvL47vvvqNfv35/et7w8HD8/PzKX4sWLbLp55Cqr0WwDx+O78jBhQMZdPssDl03m/TwrhRbnVi54ivGjh3LotfeRfd/iYjIn1m0aNEFGSM8PNxmbdlkKnnSpEmXNS08fvz4Sruj9+zZs1gsFoKDgy/YHhwczIEDBwAwm808//zz9OzZE6vVyn333XfJO5J/61IjhiIAjQI9eXV4Gxb2acZrWyP515bDFB9NpHbqDh7b48zSp9cxsVM9TInfcn1UJFFRUbi6utq7bBERqSIeeOAB5s6dW/7+1xFDW6j2y9X8fir51xtLtm7dSnR0dPlx9913Hxs2bCA2Nvayzq/lauRylVqsfPFTOjFbktl49Ocnp7gVnCViewwALm4eRF93Hf379ua6667TotkiInIBh12uprS0lF69enHo0CFbNnNZgoKCcHZ25vTp0xdsP336NCEhIXaqSmoSF2cnbmkfyoZpXdl/Xw8e6NWE2iGhJLWdwNmQ9hRYTWxct4YHHniAnjfeSMzSjzTdLCIilcImU8m/cnFxYffu3bZs4rK5uroSGRnJmjVrykcRrVYra9asYfr06fYtTmqcFsE+/HNASx7/WwvWHe7IJwmpfLn7JMXpR/E/ux+/zMMs2HiWp0+spm+zOuSsfJ1aHma6RHagffv2tGrVqsLu5hcREbFpMISfrx9csmQJTz31lK2bKpeXl8fhw4fL3ycnJ5OYmEhAQAD169dn7ty5TJo0iU6dOtGlSxdefPFF8vPzue222yqtRpHfcnYy0btZbXo3q80bN7dhU3Im/92dxjf7T5N3roC8rCKWxB6jzcGDuJbkEh/3IwAmkxPBYfXo0+tGZs34+Reb4uJizGaznroiIiKXzebXGM6YMYP333+fpk2bEhkZedH1UosXL67wNtevX0/Pnj0v2j5p0iSWLl0KwKuvvsqzzz5Leno67du35+WXXyYqKuqy29I1hmJLhmFw6Gw+3x84w/cHM9h45BwlWRl45xzHO/sEnrmpeBSc4XxwG3x7TqRNXR+cD2xg/w/LqFM3lMYNG9KoYQMaNGhAeHg4rVu3xt3d3d4fS0REroIts4fNg+GlAlp54yYTa9eutWXzNhMTE0NMTAwWi4WkpCQFQ6kUFqvBT2k5bE7OZHNyJttPZJF8JgcnSwkWl5+Xfap9ajt1TsXiVnQek2G94Ov7z19Mq6aNqefvzpv/mE+Qvx8hIcHUqVOHOnXqEBQURL169ahXr549Pp6IiPwFDh0MqzuNGIq9nS8oIeFUDjtPZrM/I5ekM/kcPJPHmdwi3IqycCs4h3vhOdwKz3Hqmj5YnV1xspTQYfOl19qsG9GFIVPvp66vO0e3r2fbD18TUqc2tWsHERT0/6+OHTvqCS4iInbg0MGwsLAQwzDKF7FOSUnhiy++oFWrVvTt29eWTVcKBUOpqs4XlHDwTD5JZ/I4mJFH0pl8TmYXcSKrkLScIgyLBZeSXFyKc3AtzsGlOBeXklwKvYLJDGkHQN1j6whN2XjJ80ff8wTNmjanrq8bSx+bgbenBw3D6xEWFkZYWBihoaE0aNCAunXrVubHFhGp9hw6GPbt25fhw4czdepUsrKyaN68Oa6urpw9e5bFixdz991327J5m1MwFEdUZrGSnlvMiaxCTmYXcfKXP9NyiknP/fnPtJwisovKMFlKcSnJ++WVW/7n6XrXYXHxAMNK222LcSnNv6id4ObtGHfv32kU4EnxqSR27YilcePGNGnShEaNGmkheBGRK+DQwTAoKIgNGzbQunVr3n77bV555RUSEhL473//y8KFC9m/f78tm7c5BUOpzgpKykjPLS4Pij///Zfg+JsAeSa/BMpKcSvKwrXo/M9T2IXnKfIK4mzdSABCj66h7onN5ec2mZyoExpGi2ZNuXfObEJDQ+31MUVEHIots4fNl6spKCjAx8cHgB9++IHhw4fj5OTEtddeS0pKiq2bF5Gr4Olq5ppAM9cE/vnTV0otVk5lF5GcWUDyuQKOnS8o/7trZiGpOUVkhHUh3y8c9/wMPH55paWe4vSpE3zp0ZUuTesRFe7L1rcep31EKzq0b0e7du2oW7cuJpOpkj6xiEjNZvMRw7Zt23LHHXcwbNgwIiIiWLlyJdHR0cTHxzNw4EDS09Nt2bzNacRQ5H8rLLVw4HQee9Jz2Jv+y5+nczl2Ng/3wkyKvGoD4J5/hlY7XuO3MdDbP4COHTpwy4hhXHvttfb5ACIiVYhDjxguXLiQsWPHMmfOHHr16lX+fOIffviBDh062Lp5m/ntcjUi8uc8XJzpUM+PDvX8LtieW1TG/oxcEk5ls+3YebaleJHotgCvnJN455zAO+cElpyTbFy3hvXZ3vwt04+/Na+DcWoffr4+REREYDbb/NuYiEiNUSnL1aSnp5OWlka7du1wcvr58cxxcXH4+vrSokULWzdvUxoxFKlYZ/OK+fF4FtuOZbIt5TxxKZlw7gTF7v6Uuf68PE6b7a/iWnAON09vIjt3ZkCfXnTv3r189QMRkerMIW8+WbhwIUOHDiUyMtIWp68yFAxFbKvMYmX7iSxWHjjDyoMZbD9+nlrpu/HLPIzv+cOYy4oAcHZx5W+Db+LvD95n54pFRGzLIYPh5MmT+frrr3F1dWXw4MEMGTKEXr164erqaovm7EbBUKRync0rZvWhs6w8kMHK/enkpR6l1pl91Dqzl3MhHajd9SZuaRdKC+spgr1ciIqK0nSziFQrDhkMAaxWK1u2bGHFihV89dVXpKWl0adPH4YOHcqgQYMICAiwVdOVRsFQxH6sVoPE1Gz+szuNTxNOknwmB6vzz798Nk9YgnfOSVw9fejTty8TRo+kSZMmdq5YROTqOWww/L39+/eXh8QdO3YQFRXFkCFDGDNmDGFhYZVVRoVSMBSpGgzDIOFUNssSU/l0VyqZh3+iVsYeap3dj7OlBIDQxs2ZMnEcgwYOsHO1IiJXrtoEw1+bMplMnDlzhuXLl7N8+XKuv/565s2bV1llVCgFQ5GqxzAMdpzI5tNdqXyyPZnCI/EEpe3EO+ckeeGRDLptJrdH1aeRnwvu7u72LldE5LI4fDBcsmQJL7zwAocOHQKgadOmzJ49mzvuuMPWTducgqFI1WaxGvxwMIO3Y4/zQ+xuSkxmSjx+voyl89H/EmgqZMqk8Qzs36/aXQMtItWTQwfDhQsXsnjxYmbMmFG+huG2bdt49dVXmTNnDv/4xz9s2bzN/HYdw6SkJAVDEQeQkVvM+ztOsiTuOAdO59B097/xzUoGwNXbj+HDRzB5/Ohqcf2ziFRfDh0Ma9euzcsvv8yYMWMu2P7xxx8zY8YMzp49a8vmbU4jhiKOxzAMth07z9uxx/li8058U34k8PRunKxl4GRm6PjJPDzjTj2KT0SqJId+8klpaSmdOnW6aHtkZCRlZWW2bl5E5CImk4nrGgVwXaMAnh3ciiWx3YlZu4eSA1upnRrH8/E5fPniJmZ2a0SUfzHNGl9Tvji/iEh1ZvMRwxkzZuDi4sLixYsv2D5v3jwKCwuJiYmxZfM2pxFDkeqhzGLlyz3pvLThEJuPZYPJhJOlhHY/voB/YBB33zGZYYMHaE1EEbE7h5tKnjt3bvnfy8rKWLp0KfXr1+faa68FIDY2luPHjzNx4kReeeWVim6+UikYilQ/CSezeXlzMp9u20/dA1/jf+4gAO5+QUycOJ6Jo27W3cwiYjcOFwx79uz51xo3mVi7dm1FN1+pFAxFqq/TucW8sjmZt1bG4nl4AwGnf8KEgdnTh4f+sYjBPa61d4kiUgM5XDCsSRQMRaq/3KIy3opN4aXv4jH2rcP/7H72dZnOkPYNuK9HY5r6GAQGBtq7TBGpIRQMqzAFQ5Gao6TMyoc7T/LM6oMcOFcEgO+5QzTdt4xON/TlwRl3Ur9+fTtXKSLVncMHw6KiInbv3k1GRgZWq/WCfUOGDLF18zalYChS81itBsv3pvP0uiMc3L6J8CPf41KSh2Ey0aJTNx6eNZWWLZrbu0wRqaYcOhiuXLmSiRMnXnK9QpPJhMVisWXzNqdgKFJzGYbBpqOZPLVqP9s3rCLkxFbcis4D0KBNJ9577SW8PdzsXKWIVDe2zB42X5hrxowZjBw5krS0NKxW6wUvRw6FMTExtGrVis6dO9u7FBGxE5PJRPfGgXw7tRtrF99L27v+ybFWwyn0qkNiWh7NntnAc+uOcC47H121IyKOwOYjhr6+viQkJNC4cWNbNmM3GjEUkd9KPlfAM+uSeG/rYQpNP48WNj36LeFGJrOn3snAvjdqsWwRuSoOPWJ48803s379els3IyJSJTQK9OT1m9tz9LFB3N+zCT6uThj558k+eYS/P7yAHgOH8+F/l+vJTyJSJdl8xLCgoICRI0dSu3Zt2rRpg4uLywX7Z86cacvmbU4jhiLyZ7IKS3ltyzFivlqPR9L68sWyXf1qM3XaNCYOH2TnCkXE0Tj0zSdLlixh6tSpuLu7ExgYeMFD6U0mE0ePHrVl8zanYCgif0VBSRlLYk/wwvLNsHcNtTL2ktJ8MD36DuCBG5vQsZ7fBd8fRUT+iEMHw5CQEGbOnMmCBQuq5XU1CoYicjlKLVY+2nmKZ5b/yP48FwwnZ0xWC533v0evnj2YP2UitWrVsneZIlKFOXQwDAgIYPv27br5RETkN6xWg6/2prNozWH27t1L811LcbKWgbMrnXv05cHptxMeHm7vMkWkCnLoYDhnzhxq167Ngw8+aMtm7EbBUESuhmEYrD10lie/SeDA5u+pcyoOc1khhslEsw7RLHp4Pg3rKyCKyP+zZfYwV+jZLsFisfDMM8/w/fff07Zt24tuPlm8eLGtSxARqbJMJhO9mtWmV7O+xI3ozJMr97F13fcEn9jGwYRYur8Rx7TexUy5tj5+7uZqeUmOiFQdNh8x7Nmz5x83bjKxdu1aWzZvcxoxFJGKti89l6fXJPHlpnhyvOoCEFCQSssjKxg96hZuGz0CLy8vO1cpIvbi0FPJ1Z2CoYjYSmp2ETFbknl9awquSRsIO7oKE2Bycadb778x586J1K9f395likglc7hguHv3biIiIv7ylMfevXtp3rw5ZrPNZ7YrnIKhiNhafnEZ78ef5MVvd5C/ZwNBaTsxW4oxMNEwoiNLXlmMv49GEEVqCocLhs7OzqSnp1O7du2/dLyvry+JiYlcc801FV2KzcTExBATE4PFYiEpKUnBUERszmo1+Gb/aZ5fvZ+9P66nzqlYysyenO06hTui6jM+ohYNA73x8fGxd6kiYkMOFwydnJyYMmUKnp6ef+n41157jX379jlUMPyVRgxFxB4STmbzyuajfBp7iHwnDwDCkldTNzWOyG49mX7rWFq3bq1Fs0WqIYcLhj169Ljsb0YfffQRdevWrehSbE7BUETs6Vx+Ce/EHee1rcco3LWa4BPbcCnNByCgXiMmjrmF4YMH/uVf1EWk6nO4YFiTKBiKSFVgsRp8u/80r2w4zI5tmwlK24Fv1jEA3GrX4+mYJXRtFKARRJFqQMGwClMwFJGq5mBGHq9vPcbH6xNwPRZLsXstzoR1oWWwN/080ugQ5MzwwQPx9va2d6kicgUUDKswBUMRqaoKSy38d3cab25LYXNyJgAtd7yBZ/5pTGZXOne7gSnjR9GuXTuNJIo4EAXDKkzBUEQcwb70XN6KTeGjdQm4HNtO4OlEXEoLAPALDuPW8eOYMOYWO1cpIn+FgmEVpmAoIo6k6JdRxDe2HGHPjliC0nbie/4wmfW6EH3zHdx5bX3aBzrj5+enx++JVFHVMhharVZOnjzp8Kv2KxiKiKPafzqXt348zr83/URWkYVSt5+/h7U98An+pecZPuwmxoy4iTp16ti5UhH5LYcOhu+++y7Lli0jJSUFX19frr/+eubMmYPZbCY0NBSLxWLL5m1OwVBEHF1RqYXPf0rjrR+Ps/7wGa7Z91/8zx3AZFjBZKJF+y7cPm4k13fr5pBPqBKpbhwyGFosFoYPH87KlSsZOHAgTZs25fz583z//fecP3+eV155hcmTJysYiohUIYfO5PF27HHe37QP4+gOgtJ34l74840r4R2788ozT1LP38POVYrUbA4ZDJ977jkWL17MunXraN68efl2q9XK4sWLeeihhygrK1MwFBGpgkrKrKzYl86/tqWwbfsOAlMTOBfclvzAxvRvUYe22Ylc36oBvXrdiKurq73LFalRHDIYRkREsGDBAsaPH3/J/c8++yz3338/VqvVFs1XGgVDEanujmUW8E7ccd6JO8Gp7CKcLCW03focztZSXDy96fu3/kwaPdIhH2sq4ogcMhh6eHiwe/dumjZtaovT211MTAwxMTFYLBaSkpIUDEWk2iuzWFl58Az/2naMdXGJBKTGE5CxB2dLCQDhTVoyZ9pddL++m50rFaneHDIYBgUFsXz5cq677rpL7k9MTOTll1/mnXfesUXzlUYjhiJSE6VmF/Hu9uMs2XKYnIM7CErbiXfuSfI6DGfymFu4Pao+FOUSEKDH8IlUNIcMhiNGjMDLy4v333//on3p6en06NGDQ4cO6RpDEREHZrUarD50hpgtx1gV9xPF7v5YnV1xc4b2cS8TEhTA2FtGMHDgQLy8vOxdrki14JDBcPfu3URHR3PzzTczf/58mjRpQmZmJitWrOCJJ56gQYMGbNu2TcFQRKSaOJZZwBtbU3g7NoXsrPM02v85vlnJALi6ezJi+DDGjR1DSEiInSsVcWwOGQwBNm7cyOTJk0lOTi7fZjabmTVrFjNmzKBBgwa6+UREpJopKrWwLDGVV7ck81NSMkGpO6idthNnSzEmJycefORRhg0eaO8yRRyWLbOHTVcq7d69O0lJScTGxnLs2DF8fX2Jjo4mICCA/Px8Hn30UVs2LyIiduDu4sykzuFM6hxO3PE2vLSxDZ/F98A/NYHaaTu4fV0em429zLq+EWlJPxEZGamFs0WqCJuMGEZHR9OhQwfat29Phw4daNOmDe7u7hXdTJWgEUMRkf/txPlCXt6czL+2HSOn+OdLiLzzUmke/xaBdUK4c/KtDB48GDc3NztXKlL1OdxU8hNPPMHu3bvZtWsXR44cwWQy0bRpU9q3b3/Bqzo8f1PBUETkr8spKuXt2OO8tCmZ06dOEJqygVoZezFh4FsrgMmTJjJ8+HA8PT3tXapIleVwwfC34uLiuOmmm+jWrRsuLi4kJCRw4MABTCYTwcHBpKam2rJ5m1MwFBG5fGUWK//ZncbzG46wO+kYISe2EJieiJNhwdPbh8+WfUJwcLC9yxSpkhz2GkOAu+++m5iYGIYNG1a+7dtvv2XKlClMmjTJ1s2LiEgVZHZ2YnSHMEa1D2XNoZY8vqox2/Z3J/jEVtwLzjL0kyQe6QPX1/OgrKyMgIAAe5csUiPYfMTQ09OTvXv30qhRowu2L1++nBdeeIF169bZsnmb04ihiEjF2HT0HE+uPsT3BzLgl0WxO5zdgtvhzYwbM5oJEybg5+dn5ypF7M+W2cOpQs92CZ07d+a99967aHubNm2Ii4uzdfMiIuIgrr8mkJVTriVudneGtP55GvlUkTOFFli6dCkDBg3mX//6F3l5eXauVKT6svmIYXx8PDfeeCPDhw9nzpw5REREUFJSwrx581ixYgUpKSm2bN7mNGIoImIbu1NzeGJ1Ep/HJ1Pn5DaCT/6Is6UEDy9vZk6fxsiRI+1doohdOPQ1hpGRkcTGxjJ9+nTat2+Pi4sLVqsVs9nMkiVLbN28iIg4qLahvnw6sRO7ejflke8a8O2uKEJObKHOqTheWneQsE6ZXNdI1x6KVCSbjxj+1vHjx0lMTMTJyYnIyEjq1q1bWU3bjEYMRUQqR2zKeR5ZeYB1e45hcXbDcHahf4sgQhM+ZnC/XgwcOFALZUuN4JDL1SxcuJChQ4cSGRlpi9NXGQqGIiKVa8ORszz83UE2J2fimZtKi51vY8IgLLwBc2bN4IYbbsD0y80rItWRQ958cvLkSfr370+9evW4++67+e677ygpKbFVcyIiUkPc0DiIjdOuY+WdUbRs2ZK9ne/hfFArTp1IYd68eYyfeCvx8fH2LlPEIdl0KtlqtbJlyxZWrFjBV199RVpaGn369GHo0KEMGjSoWqxLpRFDERH7MQyD/+5O46HvDnDySBJhyWvwzUoGTLz3yWe0btLQ3iWKVDiHnEq+lP3795eHxPj4eLp06cKQIUMYM2YMYWFhlVVGhYiJiSEmJgaLxUJSUpKCoYiIHZVZrLy34ySPrTxA9rF9eOWepLB5L+7r2YRxLbxxxkK9evXsXaZIhXDoYJiXl4e3t/dF28+cOcPy5ctZvnw5119/PfPmzbNlGTajEUMRkaqjsNTCa1uO8c81h8gsKAWgxZEV+KTtYviw4dx55x0EBgbauUqRq+PQwdDZ2ZlPP/2UESNG2LIZu1EwFBGperILS3l+wxEWbziKx7E4QlPW41KSh4ubOxPHj2PChAmXHLQQcQQOHQydnJzo3bs3+fn5mEwmOnXqxLhx4+jcubMtm600CoYiIlXX6dxi/rnmEG9sSiIg5UdCTmzB2VKMp48v98+7l4EDB9q7RJHL5pB3Jf9WQkICHTt2pFu3buzdu9ehp45FRMRxBPu48dJNERx86G/0HTGGPdfOJL1eNHl5+Tyx5gibjp6zd4kiVUqljBh+//339OnTp3zb7t27GTp0KDNnzmTOnDm2bN7mNGIoIuI49qTl8PB3B/h25yFKXX3AZKJ/01rU2fkhk8eP4frrr9caiFLlOfRUclBQEJs3b6ZFixYXbP/mm2+YM2cOSUlJtmze5hQMRUQcz7ZjmTzw7QE2HDmH39kDNNm7DIBmrSKYec9UoqKiFBClynLoqeT27dvz7rvvXrS9SZMmHD9+3NbNi4iIXCS6YQDr7o5m5Z1RXNOuCwc63E6uX0OS9u1h+vTpjJ14K9u2baMSV3QTqRJsPmL4448/0rNnT26++Wbuuece2rZtS35+PvPnzycuLo79+/fbsnmb04ihiIhjs1oNPtuVyiMrD5J2eC+hx9bjk52CyezCS0s/5boW4fYuUeQCDj2VDLBr1y5mzpzJli1byn/7cnd357PPPmPAgAG2bt6mFAxFRKqHMouVTxJTeXL1IU4m7cGtMJNzdTvSv0UdRoaV4FuYwaBBg3Bzc7N3qVLDOVwwjI6OpkOHDrRv35727dvTtm1b3N3dycjIID4+HqvVSlRUFEFBQRXddKVTMBQRqV4sVoPPf0rjiVWH2J2WA0CTnz7EL/MwHt6+3Dx8GKNuGUlISIidK5WayuGC4RNPPMHu3bvZtWsXR44cwWQy0bRp0/Kg+OurTp06Fd10pVMwFBGpngzDYFXSGZ5bf4SNiQcJPrmNgNO7cbaWgslE5+huTBo7imuvvdbepUoN43DB8Lfi4uK46aab6NatGy4uLiQkJHDgwAFMJhPBwcGkpqbasnmbUzAUEan+dqVms3jDUT7bfgSfUwnUTt2Oe2Em5voR3P3A4wxvUxcPM5jNZnuXKjWAQwfDyMhIHn74YYYNG1a+7dtvv2XKlClMmjSJJ5980pbN25yCoYhIzXE2r5il20/y+tajnDm8F4vZjQLfeni4OBGdvhrX88fp36cXfXvfSPPmzbXkjdiEQwdDT09P9u7dS6NGjS7Yvnz5cl544QXWrVtny+ZtTsFQRKTmsVoN1hw6ywfxJ/n8pzTySyw03vMJfueSMPHzj1XvgNp07tyZvj2uv+AhD1VBWVkZRUVF5c+LPnDgAKvXrefMufOcO59FXn4BxcVFFBcX0+uWyYQ3aYGr2Yn/vvEc5zPS8PLyItDfj6AAf2r5+xMeHs6gQYPs/KlqDocOhjfccAM9e/bkscceu2B7cnIyERER5Ofn27J5m1MwFBGp2fKLy/hyTzofJ5xi3d5jeJw+gP/ZA/ieP4qTYaE4oBERExZw/TWB1CpIJe/EQdq0akmTJk0IDAys0FFFwzAoKCjAzc0Ns9mM1Wrw5rvvkXz8FGmnT3PuzBlyzp+lKDcL13otKO5+J6nZRVgOx1L/wFeXPOfh1qPIDvr5IRWtdryOR37Gxe36BNF08pM0q+1FraIMYv/zNp3atyUiIoI2bdoQFhZWYZ9RHDwYxsfHc+ONNzJ8+HDmzJlDREQEJSUlzJs3jxUrVpCSkmLL5m1OwVBERH6VX1zGqqQzLN97mu/2niL/1BEAcmv9PGsWmryWusc3lR/vZHbFO7A2tYPrMume2TSsF4a/hwtb16zEw80FJycnnJyccHZ2pqSkFB//AFq0bU9BiYVN27axYfUqzp8/T07WeQpysynOz8EoK8X0t9mkuwaTnltMyy3P41qSW95mmdmDEjdf8vzCOdF0IACuRVl45qZS5uJJmdkDq9kNZxc3XN3ccHF1BZMTJRYrpaWllJSWYSorxlxaiHNZEebSAsAoD48B6btodPDLC/olJCycXj26M3LkSOrVq2fD/wI1g0MHQ/h5iHr69OmsXbsWFxcXrFYrZrOZJUuWMHbsWFs3b1MKhiIicimGYXDobD4bjpxjw5Fz/JhynpMnT+KdnYJnXjru+WdwLcrCrTgbk2Hlp6hZlLj7g2HQcePj5VPSv5UV2IwjEWMACErdToND35bvKzO7U+biRamLFyeb9KPAJxQA38zDWJ3MuPkGUKd2bUIDfAj1df/55edGqK87dbzdqOXpQi0PF/w9XPBydf7DkUzDMMgtLuNsfgln8kpIzy3myLl8ks7kk3Qmj73puZw7n4VXzim8ck/hk5WCd85xTIaVtpMfZWLfaLpfE8jp0+nUrVu34ju+BnD4YPir48ePk5iYiJOTE5GRkdXiH4SCoYiI/FW5RWXsSc9hd1oOBzLyOH6+kJRz+ZxMP81pizuYnMCwUjs1HpO1DBNWMAxMhhXDyZkij8DykTlzSR6+1gL8a9UiMKAWwX5e1PF2pbaXG3V8XH8T/typ6+OGl1vl3DFtGAbJmQVsO3aebSnnWXPoLIdSz+Jz/ihZQS3BZKKJZxn+3y+iSdNmjBp5MwMGDMDV1bVS6qsOqk0wrI4UDEVEpCJYrAY5RaVkFZaRVVhKscWK1WpgNQysBrianfBwccLTxRkPF2cCPF3wdHWM5XGSzuTx1Z50vtqTzpZj53HPO039w9/ik30cgFqBQUy+dRLDhg3D3d3dztVWfQqGVZiCoYiIyF935Gw+b8ce552442RnpBJ8YiuBp3fhZFjx8fPntVdfoWXLlvYus0pTMKzCFAxFREQuX0mZla/2prNozSH2HDlByIkteGcfp9mEh1k8rC2NAjy1DuQfUDCswhQMRURErpxhGHy97zR//yGJ+BNZYDLhZnZinF8q3hn7mD/vXt3J/Du2zB5OFXo2ERERkctgMpkY3DqE7bOv58vJXbgm0JPiMisb169hy+ZN3DJqNF988QUax6ocGjG8ShoxFBERqThFpRaeWXeERasP4nM8lnpHV+NkLSO6a1cefeQRgoKC7F2i3WnEUERERGoEdxdnFvZtxv4FvYjsNYh9kXeR7xPKti1buPmWUezcudPeJVZrCoZXKCYmhlatWtG5c2d7lyIiIlLtNAzw5Ns7olg8vifHOt1BaoMbyCw2+PJYqaaVbUhTyVdJU8kiIiK2tf90LuM/SiAh5SyGswsj29Xln93rULd2IF5eXvYur9JpKllERERqrJbBPmyb0Y1p3ZsC8N/4ZEbddhfjJkzi5MmTdq6uelEwFBERkSrP1ezEK8Pb8PYt7TCbzWR61OXk8WNMmHQbBw8etHd51YaCoYiIiDiM26Pqs37GDRR1HkVqgxvIzT7P5DvuJD4+3t6lVQsKhiIiIuJQohsGsH1OdwKvHczxJgMoKizgnmnTWb9+vb1Lc3gKhiIiIuJw6vl7sHHadTTt2pfkljdTarHwwVqNGl4ts70LEBEREbkStTxd+WHKtdzsbmaddzAJeYE0+CGJhX2b2bs0h6URQxEREXFYXm5mvrqtC8O7tgOTiUe/P8idj7/Khg0b7F2aQ1IwFBEREYfmanbig7EdGN0+FNeiLOKXf8D8++5n69at9i7N4SgYioiIiMNzdjLx/tgODOzcgqOtbsZitTJn7r0kJCTYuzSHomAoIiIi1YKLsxOfjI+k2/XdSW4xjLKyUqbPmkNycrK9S3MYCoYiIiJSbbianfjPpE5Edu3Bycb9KC7IY8rd0zh79qy9S3MICoYiIiJSrbi7OPP5rZ0Ji+rL6bAoMvKKSTx22t5lOQQFQxEREal2fNzNfH17FE6dhrK34xQmf5dKanaRvcuq8hQMRUREpFoK9XPn2zuj8fbx5URWEQNfXcNHy/5j77KqNAVDERERqbZah/jw5W2dcXV2omDtOyx+9im++eYbe5dVZSkYioiISLXWo0kQb93SlpON+2J1MvP3x59g//799i6rSlIwFBERkWpvYqdw7hzQlWPNhmAtK2XarDmcO3fO3mVVOQqGIiIiUiM8N7gVHbr2JL1eNDmZZ5k5dz5lZWX2LqtKUTAUERGRGsHs7MSyCR0xdxxEjv81HNy7m2++X2XvsqoUs70LEBEREaksQd5ufHn7tVyflc25jEN8dK4OQwwDk8lk79KqBI0YioiISI3SPsyPmLHRZAa35dNdqby+NUVTyr9QMBQREZEaZ2KncCZ3CQfDyqIXXmH8bXcoHKJgKCIiIjXUK8MiiAjxwT37JIf37+GFl1+1d0l2p2AoIiIiNZKnq5nPJnUmo+3NlLj6sOyjD4mLi7N3WXalYCgiIiI1VotgH14bG82xFjcBBvMffIScnBx7l2U3CoYiIiJSo42LrMfwPt05HXYt+VnneOixxzEMw95l2YWCoYiIiNR4Lw+LwCVyEIVeddj200GycnLtXZJdKBiKiIhIjefr7sIHE7pwpM1Ydkbcxovb0uxdkl0oGIqIiIgAXRsFMH9gJwwnM0+sTuL7hMNYLBZ7l1WpFAxFREREfrGwbzM6h/vjlZnMA/fcxjvvfWDvkiqVgqGIiIjIL1ycnfj3uA6Y/GpjtVr51xuvc/DgQXuXVWkUDEVERER+o1ltb5695TqONxuIYbUw674HKCoqsndZlULBUEREROR37ry2Ptf37M25Om04e+o4Ty9+yd4lVQoFQxEREZHfMZlMvH1LO4o63ESJmy8rPv+MrVu32bssm1MwFBEREbmE2t5uLBl/LckthlHq4snnu07auySbUzD8A8OGDaNWrVrcfPPN9i5FRERE7GRAy2AmDujBnqhZvHrMk73p1XvhawXDPzBr1izef/99e5chIiIidvbs4JY0DalFcZmV8e/HkrjrJ3uXZDMKhn+gR48e+Pj42LsMERERsTNPVzP/HtcRs8mg6LuXueueu0lNTbV3WTbhkMFw48aNDB48mNDQUEwmE19++eVFx8TExNCwYUPc3d2JiooiLi6u8gsVERGRaqFTuD8L+7Ugs04EluIiZt//YLV8KopDBsP8/HzatWtHTEzMJfcvW7aMuXPn8uijj7Jz507atWtHv379yMjIKD+mffv2REREXPSqrr8BiIiIyNV54MYmNIzuR45/I47u38Pb77xn75IqnNneBVyJ/v37079//z/cv3jxYu68805uu+02AN544w2++eYb3nnnHRYsWABAYmJihdaUk5NzwXs3Nzfc3NwqtA0RERGxH7OzEx+Mi6RzynA8t8Xw9ltvcsP119GiRQubtltcXExxcXH5+99njorkkCOGf6akpIT4+Hh69+5dvs3JyYnevXuzbZvt1h8KDw/Hz8+v/LVo0SKbtSUiIiL20ay2N0+PjOZ401+eijLf9k9FWbRo0QUZIzw83GZtOeSI4Z85e/YsFouF4ODgC7YHBwdz4MCBv3ye3r17s2vXLvLz86lXrx6fffYZ0dHRf3j8iRMn8PX1LX+v0UIREZHq6a7oBnx/sDcJmYfJ9AzgZE4JTdzdbdbeAw88wNy5c8vf5+Tk2CwcVrtgWFFWr159Wcf7+vpeEAxFRESkevr1qSjtjo/iVE4xk5btZsM912F2ts1EbGVenlbtppKDgoJwdnbm9OnTF2w/ffo0ISEhdqpKREREqpNAL1c+GNcRkwm2Jmcy9dmlZGVl2busq1btgqGrqyuRkZGsWbOmfJvVamXNmjV/OhUsIiIicjl6NgnigRubEJCxm8T/vMb0eQuwWq32LuuqOGQwzMvLIzExsfzO4uTkZBITEzl+/DgAc+fO5a233uK9995j//793H333eTn55ffpSwiIiJSER7r15ymkd3I8w3nQOIOFr/6ur1LuioOeY3hjh076NmzZ/n7Xy/InDRpEkuXLmXUqFGcOXOGhQsXkp6eTvv27Vm5cuVFN6SIiIiIXA0XZyc+vbULXdLG4rbpVT55fymR7dvQs3t3e5d2RUyGYRj2LsIRxcTEEBMTg8ViISkpiezsbN18IiIiUkOtPXSWYU9/TJPE9zG7e/L5so8ICwuzSVs5OTn4+fnZJHsoGF4lW/7HEREREcfx/PojPPPa24QfXUXDtp35zzu2mVa2ZfZwyGsMRURERKqauTdcQ89BIzjZqBff+vZk89Fz9i7psikYioiIiFQAk8nEklHtCY4aQKGzBwOXxLF+T7JD3amsYCgiIiJSQbzczHxzRxcaBnhQnJnO7Ltu58G//5OKvHIvs6Ckws71ewqGIiIiIhWonr8Hq++KJsjfB4vJmdXffMlTL7xSIeEwJ6+A0c98XAFVXpqCoYiIiEgFaxzkxQ+z+nEm6jZKXb3570fvc++DCykuLr7icx5IPkHvEWMpXvdOBVZ6IQXDKxQTE0OrVq3o3LmzvUsRERGRKqhViA/fzB7AqS6TKfIIZOOq7xgx7lbS09Mv+1zfbIxl7PgJWM+dJCuwmQ2q/ZmWq7lKWq5GRERE/kziqWxGvL0Z09aP8D93kCbDp/HefRNxMzv/z681DINnl3zMsjdfwmRYOHNND56YeQcTr2+pdQyrIgVDERER+V+yCku59eOdrN0cS26tRrSp68Mdrb0JL0ljYP9+uLq6XvQ1ccfP88izr3Bu23KsTmZyOozks0fuoLGvSQtcV1UKhiIiIvJXGIbB4g1Huf+b/VisBvUOf0/wqR9x8fSlSUQ7MP18hZ+bfxD7Q65n67HzuBWcI/zI99SKHsJX9w6jrq+7TbOHQz4rWURERMTRmEwm7u3RmBubBPHy5mS+LO6Ik6WYwIyf2B+36YJj93QOAc8gOrRswpy7+jK8TQhmZ9vfGqIRw6ukEUMRERG5ElmFpXy08xTvbEnidMYZnE1gdjIwm0y0a3YNc3q14NoGtS76Oj0ruQpTMBQREZHKpGcli4iIiIjNKRiKiIiICKBgeMW0wLWIiIhUN7rG8CrpGkMRERGpTLrGUERERERsTsHwKv36MOyreSh2TVRcXMxjjz2mfrsM6rMro367fOqzK6N+u3zqsytjy+yhqeSrdPLkScLDwzlx4gT16tWzdzkOQ1Pwl099dmXUb5dPfXZl1G+XT312ZWyZPTRiKCIiIiKAgqGIiIiI/ELPSr5Kv87E5+bmkpOTY+dqHMevfaU+++vUZ1dG/Xb51GdXRv12+dRnVyY3Nxf4/wxSkXSN4VU6evQojRs3tncZIiIiUsMcOXKEa665pkLPqWB4laxWK6mpqfj4+GAymexdjoiIiFRzhmGQm5tLaGgoTk4Ve1WggqGIiIiIALr5RERERER+oWAoIiIiIoCCoYiIiIj8QsHwKsXExNCwYUPc3d2JiooiLi7O3iVVGYsWLaJz5874+PhQp04dbrrpJg4ePHjBMUVFRUybNo3AwEC8vb0ZMWIEp0+ftlPFVc9TTz2FyWRi9uzZ5dvUZ5d26tQpxo8fT2BgIB4eHrRp04YdO3aU7zcMg4ULF1K3bl08PDzo3bs3hw4dsmPF9mWxWHjkkUdo1KgRHh4eNG7cmMcff/yC5S/UZ7Bx40YGDx5MaGgoJpOJL7/88oL9f6WPMjMzGTduHL6+vvj7+3P77beTl5dXiZ+icv1Zn5WWlnL//ffTpk0bvLy8CA0NZeLEiaSmpl5wjprWZ/C//6391tSpUzGZTLz44osXbK+IflMwvArLli1j7ty5PProo+zcuZN27drRr18/MjIy7F1albBhwwamTZvGjz/+yKpVqygtLaVv377k5+eXHzNnzhxWrFjBZ599xoYNG0hNTWX48OF2rLrq2L59O2+++SZt27a9YLv67GLnz5+na9euuLi48N1337Fv3z6ef/55atWqVX7MM888w8svv8wbb7xBbGwsXl5e9OvXj6KiIjtWbj9PP/00r7/+Oq+++ir79+/n6aef5plnnuGVV14pP0Z9Bvn5+bRr146YmJhL7v8rfTRu3Dj27t3LqlWr+Prrr9m4cSNTpkyprI9Q6f6szwoKCti5cyePPPIIO3fu5PPPP+fgwYMMGTLkguNqWp/B//639qsvvviCH3/8kdDQ0Iv2VUi/GXLFunTpYkybNq38vcViMUJDQ41FixbZsaqqKyMjwwCMDRs2GIZhGFlZWYaLi4vx2WeflR+zf/9+AzC2bdtmrzKrhNzcXKNp06bGqlWrjBtuuMGYNWuWYRjqsz9y//33G926dfvD/Var1QgJCTGeffbZ8m1ZWVmGm5ub8fHHH1dGiVXOwIEDjcmTJ1+wbfjw4ca4ceMMw1CfXQpgfPHFF+Xv/0of7du3zwCM7du3lx/z3XffGSaTyTh16lSl1W4vv++zS4mLizMAIyUlxTAM9Zlh/HG/nTx50ggLCzP27NljNGjQwHjhhRfK91VUv2nE8AqVlJQQHx9P7969y7c5OTnRu3dvtm3bZsfKqq7s7GwAAgICAIiPj6e0tPSCPmzRogX169ev8X04bdo0Bg4ceEHfgPrsjyxfvpxOnToxcuRI6tSpQ4cOHXjrrbfK9ycnJ5Oenn5Bv/n5+REVFVVj++26665jzZo1JCUlAbBr1y42b95M//79AfXZX/FX+mjbtm34+/vTqVOn8mN69+6Nk5MTsbGxlV5zVZSdnY3JZMLf3x9Qn/0Rq9XKhAkTmD9/Pq1bt75of0X1mx6Jd4XOnj2LxWIhODj4gu3BwcEcOHDATlVVXVarldmzZ9O1a1ciIiIASE9Px9XVtfybwa+Cg4NJT0+3Q5VVwyeffMLOnTvZvn37RfvUZ5d29OhRXn/9debOncuDDz7I9u3bmTlzJq6urkyaNKm8by71/2tN7bcFCxaQk5NDixYtcHZ2xmKx8OSTTzJu3DgA9dlf8Ff6KD09nTp16lyw32w2ExAQoH7k52um77//fsaMGYOvry+gPvsjTz/9NGazmZkzZ15yf0X1m4KhVIpp06axZ88eNm/ebO9SqrQTJ04wa9YsVq1ahbu7u73LcRhWq5VOnTrxz3/+E4AOHTqwZ88e3njjDSZNmmTn6qqmTz/9lA8//JCPPvqI1q1bk5iYyOzZswkNDVWfSaUoLS3llltuwTAMXn/9dXuXU6XFx8fz0ksvsXPnTps/ZU1TyVcoKCgIZ2fni+4GPX36NCEhIXaqqmqaPn06X3/9NevWraNevXrl20NCQigpKSErK+uC42tyH8bHx5ORkUHHjh0xm82YzWY2bNjAyy+/jNlsJjg4WH12CXXr1qVVq1YXbGvZsiXHjx8HKO8b/f/6/+bPn8+CBQsYPXo0bdq0YcKECcyZM4dFixYB6rO/4q/0UUhIyEU3JJaVlZGZmVmj+/HXUJiSksKqVavKRwtBfXYpmzZtIiMjg/r165f/bEhJSeHee++lYcOGQMX1m4LhFXJ1dSUyMpI1a9aUb7NaraxZs4bo6Gg7VlZ1GIbB9OnT+eKLL1i7di2NGjW6YH9kZCQuLi4X9OHBgwc5fvx4je3DXr168dNPP5GYmFj+6tSpE+PGjSv/u/rsYl27dr1oKaSkpCQaNGgAQKNGjQgJCbmg33JycoiNja2x/VZQUHDRM1adnZ2xWq2A+uyv+Ct9FB0dTVZWFvHx8eXHrF27FqvVSlRUVKXXXBX8GgoPHTrE6tWrCQwMvGC/+uxiEyZMYPfu3Rf8bAgNDWX+/Pl8//33QAX225XfMyOffPKJ4ebmZixdutTYt2+fMWXKFMPf399IT0+3d2lVwt133234+fkZ69evN9LS0spfBQUF5cdMnTrVqF+/vrF27Vpjx44dRnR0tBEdHW3Hqque396VbBjqs0uJi4szzGaz8eSTTxqHDh0yPvzwQ8PT09P497//XX7MU089Zfj7+xtfffWVsXv3bmPo0KFGo0aNjMLCQjtWbj+TJk0ywsLCjK+//tpITk42Pv/8cyMoKMi47777yo9Rn/28QkBCQoKRkJBgAMbixYuNhISE8jto/0of/e1vfzM6dOhgxMbGGps3bzaaNm1qjBkzxl4fyeb+rM9KSkqMIUOGGPXq1TMSExMv+NlQXFxcfo6a1meG8b//rf3e7+9KNoyK6TcFw6v0yiuvGPXr1zdcXV2NLl26GD/++KO9S6oygEu+3n333fJjCgsLjXvuuceoVauW4enpaQwbNsxIS0uzX9FV0O+Dofrs0lasWGFEREQYbm5uRosWLYx//etfF+y3Wq3GI488YgQHBxtubm5Gr169jIMHD9qpWvvLyckxZs2aZdSvX99wd3c3rrnmGuOhhx664Iez+sww1q1bd8nvY5MmTTIM46/10blz54wxY8YY3t7ehq+vr3HbbbcZubm5dvg0lePP+iw5OfkPfzasW7eu/Bw1rc8M43//W/u9SwXDiug3k2H8Zpl7EREREamxdI2hiIiIiAAKhiIiIiLyCwVDEREREQEUDEVERETkFwqGIiIiIgIoGIqIiIjILxQMRURERARQMBQRERGRXygYioiIiAigYCgiNYxhGCxevJhGjRrh6enJTTfdRHZ2tl1r6tGjByaTCZPJRGJi4p8eN3v27Apt+9Zbby1v+8svv6zQc4uI41EwFJEaZf78+bz++uu89957bNq0ifj4eB577DF7l8Wdd95JWloaERERldruSy+9RFpaWqW2KSJVl4KhiNQYsbGxLF68mGXLltG9e3ciIyO58847+fbbb+1dGp6enoSEhGA2myu1XT8/P0JCQiq1TRGpuhQMRaTGeO655+jVqxcdO3Ys3xYcHMzZs2ftWNWl5efnM3HiRLy9valbty7PP//8RcdYrVYWLVpEo0aN8PDwoF27dvznP/8p35+bm8u4cePw8vKibt26vPDCCzaZjhaR6kPBUERqhOLiYr755huGDRt2wfaioiL8/PzsVNUfmz9/Phs2bOCrr77ihx9+YP369ezcufOCYxYtWsT777/PG2+8wd69e5kzZw7jx49nw4YNAMydO5ctW7awfPlyVq1axaZNmy46h4jIb1XunIWIiJ3s3LmTwsJC7r33Xu67777y7aWlpfTs2dOOlV0sLy+PJUuW8O9//5tevXoB8N5771GvXr3yY4qLi/nnP//J6tWriY6OBuCaa65h8+bNvPnmm3Ts2JH33nuPjz76qPwc7777LqGhoZX/gUTEYSgYikiNkJSUhJeX10V3/Q4cOJCuXbvap6g/cOTIEUpKSoiKiirfFhAQQPPmzcvfHz58mIKCAvr06XPB15aUlNChQweOHj1KaWkpXbp0Kd/n5+d3wTlERH5PwVBEaoScnByCgoJo0qRJ+baUlBQOHTrEiBEjAHjnnXd48cUXMZlM9OnTh+eee+6i8xw7doyhQ4cSERFBXFwcvXv3pl+/fixatIj8/Hy++OILmjZtavPPk5eXB8A333xDWFjYBfvc3NzIzMy0eQ0iUv0oGIpIjRAUFER2djaGYWAymQB48sknGTBgAK1ateKnn37ihRdeYNOmTfj7+/9psNq/fz+ffvopTZo0ISIiAm9vb2JjY3nzzTd59dVXeemll66q1saNG+Pi4kJsbCz169cH4Pz58yQlJXHDDTcA0KpVK9zc3Dh+/Hj5tt/y9/fHxcWF7du3l58jOzubpKQkunfvflX1iUj1pWAoIjXCjTfeSFFREU899RSjR4/mww8/ZMWKFcTFxQGwbt06Ro0ahb+/P/Dz1O0fad68efmUbMuWLenduzcAbdq0qZClb7y9vbn99tuZP38+gYGB1KlTh4ceeggnp/+/X9DHx4d58+YxZ84crFYr3bp1Izs7my1btuDr68ukSZOYNGkS8+fPJyAggDp16vDoo4/i5ORUHoxFRH5PwVBEaoTg4GCWLl3K/Pnzefzxx7nxxhvZvHkz4eHhl30uNze38r87OTmVv3dycsJisVRIvc8++yx5eXkMHjwYHx8f7r333oue0PL4449Tu3ZtFi1axNGjR/H396djx448+OCDACxevJipU6cyaNAgfH19ue+++zhx4gTu7u4VUqOIVD8KhiJSY4waNYpRo0Zdct+NN97ImDFjmDFjBn5+fmRmZv7pqKGteXt788EHH/DBBx+Ub5s/f/4Fx5hMJmbNmsWsWbMueQ4fHx8+/PDD8vf5+fn8/e9/Z8qUKbYpWkQcntYxFBEBIiIimDVrFl27dqV9+/Y89dRTALRv3/6Kz3k5X/vaa6/h7e3NTz/9dMXt/V5CQgIff/wxR44cYefOnYwbNw6AoUOHlh8zdepUvL29K6xNEXFsJsMwDHsXISJSk506dYrCwkIA6tevj6ura4WcNyEhgTvuuIODBw/i6upKZGQkixcvpk2bNuXHZGRkkJOTA0DdunXx8vKqkLZFxDEpGIqIiIgIoKlkEREREfmFgqGIiIiIAAqGIiIiIvILBUMRERERARQMRUREROQXCoYiIiIiAigYioiIiMgvFAxFREREBFAwFBEREZFfKBiKiIiICKBgKCIiIiK/+D8HwVLP/3/9pAAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "max |Ay(CC) - Ay(DWBA)| = 0.0162\n", - "max relative cross-section difference = 0.0266\n" - ] - } - ], - "source": [ - "d = reference\n", - "workspace_dwba = quasielastic_pn.Workspace(\n", - " d[\"reaction\"],\n", - " d[\"kinematics_entrance\"],\n", - " d[\"kinematics_exit\"],\n", - " jitr.rmatrix.Solver(d[\"nbasis\"]),\n", - " ANGLES,\n", - " LMAX,\n", - " d[\"channel_radius_fm\"],\n", - " tmatrix_abs_tol=0.0,\n", - ")\n", - "rgrid = d[\"workspace\"].radial_grid()\n", - "U_p, U_p_so, U_coulomb = omp_p(\n", - " rgrid, d[\"reaction\"], d[\"kinematics_entrance\"], *params_p\n", - ")\n", - "U_n, U_n_so, _ = omp_n(rgrid, d[\"exit_reaction\"], d[\"kinematics_exit\"], *params_n)\n", - "\n", - "cc = d[\"workspace\"].observables(U_coulomb, U_p, U_p_so, U_n, U_n_so)\n", - "dwba = workspace_dwba.observables(U_coulomb, U_p, U_p_so, U_n, U_n_so)\n", - "\n", - "fig, (ax_ay, ax_xs) = plt.subplots(\n", - " 2, 1, figsize=(7, 7), sharex=True, height_ratios=[1, 1]\n", - ")\n", - "fig.subplots_adjust(hspace=0.08)\n", - "for ax, cc_y, dwba_y in [(ax_ay, cc.Ay, dwba.Ay), (ax_xs, cc.dsdo, dwba.dsdo)]:\n", - " ax.plot(angles_deg, cc_y, color=\"#0072B2\", lw=1.6, label=\"coupled channels\")\n", - " ax.plot(angles_deg, dwba_y, color=JLMB_INK, lw=1.3, ls=\"--\", label=\"DWBA\")\n", - " ax.tick_params(direction=\"in\", top=True, right=True)\n", - "ax_ay.errorbar(\n", - " d[\"theta\"],\n", - " d[\"Ay\"],\n", - " yerr=d[\"Ay_err\"],\n", - " fmt=\"o\",\n", - " ms=3.5,\n", - " color=\"k\",\n", - " lw=0.9,\n", - " capsize=1.5,\n", - " label=\"Gosset (1976)\",\n", - ")\n", - "ax_ay.axhline(0.0, color=\"0.75\", lw=0.7, zorder=0)\n", - "ax_ay.set_ylabel(r\"$A_y$\")\n", - "ax_ay.set_ylim(-1.05, 1.05)\n", - "ax_ay.legend(loc=\"lower left\", fontsize=9, frameon=False)\n", - "ax_xs.set_yscale(\"log\")\n", - "ax_xs.set_ylabel(r\"$d\\sigma/d\\Omega$ [mb/sr]\")\n", - "ax_xs.set_xlabel(r\"$\\theta_{\\rm c.m.}$ [deg]\")\n", - "ax_xs.set_xlim(0, 140)\n", - "fig.suptitle(rf\"{d['label']}$(\\vec{{p}},n)$ to the IAS, KD03 parameters\", y=0.92)\n", - "plt.show()\n", - "\n", - "print(f\"max |Ay(CC) - Ay(DWBA)| = {np.max(np.absolute(cc.Ay - dwba.Ay)):.4f}\")\n", - "print(\n", - " f\"max relative cross-section difference = {np.max(np.absolute(cc.dsdo / dwba.dsdo - 1)):.4f}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "588d38e0", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "Propagating three uncertainty-quantified global optical potentials through an exact\n", - "coupled-channels Lane calculation reproduces the qualitative structure of the Gosset *et al.*\n", - "analyzing powers without any adjustment to these data. For KDUQ and CHUQ the sign of the forward\n", - "extremum and its mass dependence come out right, and so does the sign of the mid-angle plateau —\n", - "but at about half the measured depth, and the posterior bands nowhere near cover the data.\n", - "\n", - "That gap is the point of the original paper. The analyzing power of a quasi-elastic $(p,n)$\n", - "reaction depends on the isovector spin-orbit part of the Lane potential, which global elastic\n", - "analyses barely constrain. Here it is not a fitted quantity at all: it is the difference between\n", - "two spin-orbit potentials that were each calibrated to elastic scattering, so its uncertainty is\n", - "not represented in the bands above, and the spread between KDUQ, CHUQ, WLH and JLMB is a much\n", - "better picture of how poorly it is known than any one band is. Gosset *et al.* needed\n", - "$V^{(1)}_{so} \\approx 4$-6 MeV to fit these data, against the $\\approx 2$ MeV a simple\n", - "free-nucleon-force argument predicts. The measurements remain a good candidate for calibrating\n", - "that term directly." - ] } ], "metadata": { diff --git a/pyproject.toml b/pyproject.toml index 96e36762..f167a2b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ docs = [ examples = [ "matplotlib>=3.10.3", - "exfor_tools", + "exfor_tools>=1.3", "nbmake", "nbval", "ipykernel", diff --git a/uv.lock b/uv.lock index 877f8077..e8e3f39d 100644 --- a/uv.lock +++ b/uv.lock @@ -667,17 +667,18 @@ wheels = [ [[package]] name = "exfor-tools" -version = "1.2" +version = "1.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "matplotlib" }, { name = "numpy" }, + { name = "pandas" }, { name = "periodictable" }, { name = "x4i3" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/e1/f1/6cf8211da8144189a8ffae98b43e5ff8dfb52a57395c95aad4281636823c/exfor_tools-1.2.tar.gz", hash = "sha256:e5d601bced1c4d7e9693bb8e71a9937c7057acf71584f2e6de71d0a30ed14bd2", size = 2566320, upload-time = "2026-03-02T16:48:10.999Z" } +sdist = { url = "https://files.pythonhosted.org/packages/51/64/7d6d361de3ca2745c06cdabd11c35deb2141d3787ee539aa43ceb30f100b/exfor_tools-1.3.tar.gz", hash = "sha256:a71bda778238490b58c0662d8f34e6e159de2dc8fff23ec201bbffc5cdcc0f47", size = 2570783, upload-time = "2026-09-24T22:28:00.054Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/0d/9d/a9871c61eb68ee88d0bfc7104ac02f3380d556fa014bc4c16a7e829f9939/exfor_tools-1.2-py3-none-any.whl", hash = "sha256:03f0c9d1e794f3836b4c9c0ac85fa19111cbb7fa44b97e106f9a10abdd2e2fda", size = 25321, upload-time = "2026-03-02T16:48:09.51Z" }, + { url = "https://files.pythonhosted.org/packages/0d/b9/0589d565afeb6c070eb6fb9bd655cd47ef2f645509b152b1dae2398b946e/exfor_tools-1.3-py3-none-any.whl", hash = "sha256:fdffd2ec6fd51ea256f5b535fea69ab2bc3a92efcb0637de7db80bdcd28271f3", size = 28258, upload-time = "2026-09-24T22:27:58.712Z" }, ] [[package]] @@ -1087,7 +1088,7 @@ dev = [ { name = "black", extras = ["jupyter"], specifier = ">=26.3.1" }, { name = "corner", specifier = ">=2.0.0" }, { name = "dynesty", specifier = ">=3.0.0" }, - { name = "exfor-tools" }, + { name = "exfor-tools", specifier = ">=1.3" }, { name = "flake8" }, { name = "flake8-pyproject", specifier = ">=1.2.4" }, { name = "furo" }, @@ -1114,7 +1115,7 @@ examples = [ { name = "black", extras = ["jupyter"], specifier = ">=26.3.1" }, { name = "corner", specifier = ">=2.0.0" }, { name = "dynesty", specifier = ">=3.0.0" }, - { name = "exfor-tools" }, + { name = "exfor-tools", specifier = ">=1.3" }, { name = "ipykernel" }, { name = "jupyter" }, { name = "matplotlib", specifier = ">=3.10.3" }, From 83557562f761276cbebd528794f77500fe75aef0 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 24 Sep 2026 21:11:58 -0400 Subject: [PATCH 11/11] ruff --- examples/notebooks/qepn_analyzing_power.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/notebooks/qepn_analyzing_power.ipynb b/examples/notebooks/qepn_analyzing_power.ipynb index c501d509..121485b7 100644 --- a/examples/notebooks/qepn_analyzing_power.ipynb +++ b/examples/notebooks/qepn_analyzing_power.ipynb @@ -101,7 +101,7 @@ ")\n", "from jitr.optical_potentials import chuq, kduq, wlh\n", "from jitr.utils.density import density_table\n", - "from jitr.xs import lane_pn, quasielastic_pn\n", + "from jitr.xs import lane_pn\n", "\n", "# set JITR_QUICK=1 to thin the posterior draws; used by the CI notebook run\n", "QUICK = os.environ.get(\"JITR_QUICK\", \"0\") == \"1\"" @@ -134,6 +134,7 @@ ], "source": [ "import exfor_tools\n", + "\n", "print(exfor_tools.__version__)" ] }, @@ -162,6 +163,7 @@ "source": [ "from exfor_tools import ExforEntry\n", "from exfor_tools.reaction import Reaction as ExforReaction\n", + "\n", "# the nine targets of Table I, in the order the paper presents them\n", "TARGETS = [\n", " (49, 22),\n", @@ -620,7 +622,7 @@ " for name, color in BANDS.items():\n", " lo, hi = predictions[name][i][\"Ay\"]\n", " ax.fill_between(angles_deg, lo, hi, color=color, alpha=0.35, lw=0, label=name)\n", - " #ax.plot(angles_deg, predictions[name][i][\"Ay_median\"], color=color, lw=1.2)\n", + " # ax.plot(angles_deg, predictions[name][i][\"Ay_median\"], color=color, lw=1.2)\n", " ax.plot(angles_deg, curve.Ay, color=JLMB_INK, lw=1.2, ls=\"--\", label=\"JLMB\")\n", " ax.errorbar(\n", " d[\"theta\"],\n",