examples/Decay.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import proposal as pp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['axes.labelsize'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAMPLE_SIZE = int(1e7)\n",
    "pp.InterpolationSettings.tables_path = \"/tmp\" # save interpolation tables to folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decay module can calculate the energy when a particle with finite lifetime will decay.\n",
    "\n",
    "*When executed for the first time, the following cell will take some time since interpolation tables are built*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = {\n",
    "    \"particle_def\": pp.particle.MuMinusDef(),\n",
    "    \"target\": pp.medium.StandardRock(),\n",
    "    \"interpolate\": True,\n",
    "    \"cuts\": pp.EnergyCutSettings(500, 1, False)\n",
    "}\n",
    "\n",
    "cross = pp.crosssection.make_std_crosssection(**args)\n",
    "decay = pp.make_decay(cross, args[\"particle_def\"], True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take muons with an initial energy of $10^{14} \\mathrm{MeV}$ and sample the energy when the muons will decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "decay_energy = decay.energy_decay([1e14]*SAMPLE_SIZE, np.random.rand(SAMPLE_SIZE), [args[\"target\"].mass_density]*SAMPLE_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(decay_energy, bins=np.geomspace(args[\"particle_def\"].mass, 1e14, 40))\n",
    "plt.xlabel(r'decay energy $\\,/\\, \\mathrm{MeV} $')\n",
    "plt.ylabel(\"counts\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}