{ "cells": [ { "cell_type": "markdown", "id": "a28bd278-dff6-493d-9333-7b08ed05b425", "metadata": {}, "source": [ "## `jitr` quickstart example\n", "\n", "We will \n", "- compile a solver for a particular reaction and kinematics\n", "- define a parametric potential and generate samples of the interaction parameters\n", "- propagate parameter samples through our compiled solver to generate predictive distributions of reaction observables\n", "\n", "To illustrate, we will use the example of $\\alpha$ elastic scattering on $^{48}$Ca at 29 MeV, comparing to experimental data from [EXFOR](https://www-nds.iaea.org/exfor/servlet/X4sGetSubent?reqx=14669&subID=150567004&plus=1)." ] }, { "cell_type": "code", "execution_count": 1, "id": "2c9c4d06-cf1c-41ae-b7e1-02847faa70e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compiling solver for 48-Ca(alpha,el) at 28.2 MeV\n", " - channel radius 11.19 fm\n", " - 40 nodes\n", " - 180 partial waves\n", "Done!\n" ] } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy import stats\n", "from tqdm import tqdm\n", "\n", "from jitr.optical_potentials.potential_forms import (\n", " coulomb_charged_sphere as coulomb,\n", ")\n", "from jitr.optical_potentials.potential_forms import (\n", " woods_saxon_safe as ws,\n", ")\n", "from jitr.reactions.reaction import Reaction\n", "from jitr.rmatrix import Solver as SolverKernel\n", "from jitr.utils import utils\n", "from jitr.xs import elastic\n", "\n", "# define reaction system\n", "alpha = (4, 2)\n", "Ca48 = (48, 20)\n", "reaction = Reaction(target=Ca48, projectile=alpha, process=\"El\")\n", "\n", "# calculate kinematics for a given lab energy\n", "energy_lab = 28.2\n", "kinematics = reaction.kinematics(energy_lab)\n", "\n", "# set the channel radius, number of nodes, and number of partial waves\n", "interaction_range_fm = 1.2 * (48 ** (1 / 3) + 4 ** (1 / 3)) + 2\n", "channel_radius_dimensionless = utils.suggested_dimensionless_channel_radius(\n", " interaction_range_fm, kinematics.k\n", ")\n", "channel_radius = channel_radius_dimensionless / kinematics.k\n", "N = utils.suggested_basis_size(channel_radius_dimensionless)\n", "lmax = 180\n", "\n", "# build a solver for the system and reaction of interest\n", "print(f\"Compiling solver for {reaction} at {energy_lab} MeV\")\n", "print(f\" - channel radius {channel_radius:1.2f} fm\")\n", "print(f\" - {N} nodes\")\n", "print(f\" - {lmax} partial waves\")\n", "\n", "solver = elastic.DifferentialWorkspace.build_from_system(\n", " reaction=reaction,\n", " kinematics=kinematics,\n", " channel_radius_fm=channel_radius,\n", " solver=SolverKernel(N),\n", " lmax=lmax,\n", " angles=np.linspace(0.1, np.pi, 180),\n", ")\n", "rgrid = solver.radial_grid()\n", "# jit warmup\n", "_ = solver.xs(central_potential=np.zeros_like(solver.radial_grid()))\n", "print(\"Done!\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "9db3e076-9748-4093-99bf-ce14a3db6b71", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running 1000 calculations...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 168.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# define interaction\n", "def U_central(r, Vv, Wv, Rv, av):\n", " fr = ws(r, Rv, av)\n", " return -(Vv * fr + 1j * Wv * fr)\n", "\n", "\n", "def V_Coulomb(r, Zz, RC):\n", " return coulomb(r, Zz, RC)\n", "\n", "\n", "# define parameter distribution and draw samples\n", "# just\n", "param_means = np.array([185, 20, 1.0, 0.6, 1.2])\n", "param_std_devs = np.array([6, 2, 0.05, 0.05, 0.01])\n", "num_samples = 1000\n", "param_draws = stats.multivariate_normal(\n", " mean=param_means, cov=np.diag(param_std_devs) ** 2\n", ").rvs(num_samples)\n", "\n", "print(f\"Running {num_samples} calculations...\")\n", "prediction_samples = np.zeros((num_samples, solver.angles.size))\n", "for i in tqdm(range(param_draws.shape[0])):\n", " Vv, Wv, rv, av, rC = param_draws[i]\n", " A_factor = reaction.target.A ** (1 / 3) + reaction.projectile.A ** (1 / 3)\n", " Zz = reaction.target.Z * reaction.projectile.Z\n", " xs = solver.xs(\n", " central_potential=U_central(\n", " rgrid,\n", " Vv,\n", " Wv,\n", " rv * A_factor,\n", " av,\n", " ),\n", " coulomb_potential=V_Coulomb(rgrid, Zz, rC * A_factor),\n", " )\n", " prediction_samples[i, :] = xs.dsdo / solver.rutherford\n", "\n", "print(\"Done!\")" ] }, { "cell_type": "markdown", "id": "701613c8-0f40-446c-b09b-0094196e02ad", "metadata": {}, "source": [ "We will compare to some experimental data from [EXFOR](https://www-nds.iaea.org/exfor/servlet/X4sGetSubent?reqx=14669&subID=150567004&plus=1) " ] }, { "cell_type": "code", "execution_count": 3, "id": "f81025e9-5111-4917-89dd-2de8722a9bab", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv(\"./alpha_ca48_ratio_ruth.txt\", names=[\"angle\", \"xs\"], sep=r\"\\s+\")" ] }, { "cell_type": "markdown", "id": "84812730-d08d-4b51-825e-0244fd804f2e", "metadata": {}, "source": [ "### Plot predictive cross section distribution" ] }, { "cell_type": "code", "execution_count": 4, "id": "9edaf99e-23d1-48fd-a59c-75d54f7138d3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.scatter(df[\"angle\"], df[\"xs\"], color=\"k\", marker=\".\")\n", "\n", "plt.fill_between(\n", " np.rad2deg(solver.angles),\n", " *np.percentile(prediction_samples, [16, 84], axis=0),\n", " alpha=0.5,\n", ")\n", "plt.yscale(\"log\")\n", "plt.xlabel(r\"$\\theta$ [deg]\")\n", "plt.ylabel(r\"$(\\frac{d\\sigma}{d\\Omega}) / (\\frac{d\\sigma}{d\\Omega})_{R}$\")\n", "plt.title(f\"${reaction.reaction_latex}$ at {kinematics.Elab:1.0f} MeV\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c1bf7a9b-e4a5-4afa-8730-43257a5c8b51", "metadata": {}, "source": [ "This looks like a reasonable prior! The next steps would be to use Bayesian calibration to determine a posterior..." ] } ], "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 }