Projektpraktikum/electron_lost_found.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#look at particles only from Signal\n",
    "allcolumns = file.arrays()\n",
    "tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] \n",
    "\n",
    "#ak.num(tracked, axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lost\n",
    "l_eph = lost[\"brem_photons_pe\"]\n",
    "ak.nan_to_num(l_eph)\n",
    "l_pT = lost[\"pt\"]\n",
    "l_sci_x = lost[\"scifi_hit_pos_x\"]\n",
    "ak.nan_to_num(l_sci_x)\n",
    "\n",
    "#found\n",
    "f_eph = tracked[\"brem_photons_pe\"]\n",
    "ak.nan_to_num(f_eph)\n",
    "f_pT = tracked[\"pt\"]\n",
    "f_sci_x = tracked[\"scifi_hit_pos_x\"]\n",
    "ak.nan_to_num(f_sci_x)\n",
    "\n",
    "l_sci_x, l_pT = ak.broadcast_arrays(l_sci_x, l_pT)\n",
    "f_sci_x, f_pT = ak.broadcast_arrays(f_sci_x, f_pT)\n",
    "\n",
    "l_sci_x = ak.to_numpy(ak.flatten(l_sci_x))\n",
    "l_pT = ak.to_numpy(ak.flatten(l_pT))\n",
    "f_sci_x = ak.to_numpy(ak.flatten(f_sci_x))\n",
    "f_pT = ak.to_numpy(ak.flatten(f_pT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "ax0.hist2d(l_sci_x, l_pT, bins=100,  cmap=plt.cm.jet)\n",
    "ax0.set_xlabel(\"scifi x\")\n",
    "ax0.set_ylabel(r\"$p_T$\")\n",
    "ax0.set_title(\"lost electron positions in the scifi in regard to their transverse momentum\")\n",
    "pcm0 = ax0.pcolormesh([l_sci_x,l_pT])\n",
    "plt.colorbar(pcm0,ax=ax0)\n",
    "\n",
    "ax1.hist2d(f_sci_x, f_pT, bins=100,  cmap=plt.cm.jet)\n",
    "ax1.set_xlabel(\"scifi x\")\n",
    "ax1.set_ylabel(r\"$p_T$\")\n",
    "ax1.set_title(\"found electron positions in the scifi in regard to their transverse momentum\")\n",
    "pcm1 = ax1.pcolormesh([f_sci_x,f_pT])\n",
    "plt.colorbar(pcm1,ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can see that the lost electrons cover a wider spread in the x direction of the scifi tracker,\n",
    "while most of those have low pT \n",
    "\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.09e+04], [8.66e+03], [8.07e+04], ..., [5.63e+03], [6.29e+03], [2.26e+03]]\n",
      "[4.62e+04, 9.36e+03, 1.34e+05, 5.63e+04, ..., 2.01e+04, 6.94e+03, 7.83e+03]\n",
      "8657.132\n",
      "9355.866625028413\n"
     ]
    }
   ],
   "source": [
    "energy_found = tracked[\"energy\"]\n",
    "energy_found = energy_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(energy_found)\n",
    "\n",
    "e_ph_found = tracked[\"brem_photons_pe\"]\n",
    "e_ph_found = e_ph_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(e_ph_found, nan=[0])\n",
    "e_ph_found = ak.sum(e_ph_found, axis=-1, keepdims=True)\n",
    "print(e_ph_found)\n",
    "print(energy_found)\n",
    "\n",
    "energy_lost = lost[\"energy\"]\n",
    "energy_lost = energy_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(energy_lost)\n",
    "\n",
    "e_ph_lost = lost[\"brem_photons_pe\"]\n",
    "e_ph_lost = e_ph_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(e_ph_lost)\n",
    "e_ph_lost = ak.sum(e_ph_lost, axis=-1,keepdims=True)\n",
    "\n",
    "#e_ph_found, energy_found = ak.broadcast_arrays(e_ph_found, energy_found)\n",
    "#e_ph_lost, energy_lost = ak.broadcast_arrays(e_ph_lost, energy_lost)\n",
    "\n",
    "e_ph_found = ak.to_numpy(ak.flatten(e_ph_found))\n",
    "energy_found = ak.to_numpy(energy_found)\n",
    "\n",
    "e_ph_lost = ak.to_numpy(ak.flatten(e_ph_lost))\n",
    "energy_lost = ak.to_numpy(energy_lost)\n",
    "\n",
    "print(e_ph_found[1])\n",
    "print(energy_found[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_e_found = e_ph_found/energy_found\n",
    "q_e_lost = e_ph_lost/energy_lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q_e_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(q_e_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can clearly see that lost electrons are responsible for higher energy photons\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "ax0.hist2d(e_ph_found, energy_found, density=True, bins=200,  cmap=plt.cm.jet, range=[[0,100000],[0,100000]])\n",
    "ax0.set_xlabel(r\"$E_\\gamma$\")\n",
    "ax0.set_ylabel(r\"$E_e$\")\n",
    "ax0.set_title(\"found electron energy against photon energy\")\n",
    "pcm0 = ax0.pcolormesh([e_ph_found, energy_found])\n",
    "plt.colorbar(pcm0,ax=ax0)\n",
    "\n",
    "ax1.hist2d(e_ph_lost, energy_lost, density=True, bins=200, cmap=plt.cm.jet, range=[[0,100000],[0,100000]])\n",
    "ax1.set_xlabel(r\"$E_\\gamma$\")\n",
    "ax1.set_ylabel(r\"$E_e$\")\n",
    "ax1.set_title(\"lost electron energy against photon energy\")\n",
    "pcm1 = ax1.pcolormesh([e_ph_lost, energy_lost])\n",
    "plt.colorbar(pcm1,ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "concentrated at the E_ph/E_0~1 line especially at lower energies.\n",
    "lost E_ph to E_0: fewer entries at lower q_e\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "brem_vtx_x_found = tracked[\"brem_vtx_x\"]\n",
    "brem_vtx_x_found = brem_vtx_x_found[tracked[\"brem_vtx_x_length\"]!=0]\n",
    "vtx_x_fit = brem_vtx_x_found[1]\n",
    "brem_vtx_x_found = ak.to_numpy(ak.flatten(brem_vtx_x_found))\n",
    "\n",
    "brem_vtx_z_found = tracked[\"brem_vtx_z\"]\n",
    "brem_vtx_z_found = brem_vtx_z_found[tracked[\"brem_vtx_z_length\"]!=0]\n",
    "vtx_z_fit = brem_vtx_z_found[1]\n",
    "brem_vtx_z_found = ak.to_numpy(ak.flatten(brem_vtx_z_found))\n",
    "\n",
    "brem_vtx_x_lost = lost[\"brem_vtx_x\"]\n",
    "brem_vtx_x_lost = brem_vtx_x_lost[lost[\"brem_vtx_x_length\"]!=0]\n",
    "brem_vtx_x_lost = ak.to_numpy(ak.flatten(brem_vtx_x_lost))\n",
    "\n",
    "brem_vtx_z_lost = lost[\"brem_vtx_z\"]\n",
    "brem_vtx_z_lost = brem_vtx_z_lost[lost[\"brem_vtx_z_length\"]!=0]\n",
    "brem_vtx_z_lost = ak.to_numpy(ak.flatten(brem_vtx_z_lost))\n",
    "\n",
    "vtx_x_fit= ak.to_numpy(vtx_x_fit)\n",
    "vtx_z_fit = ak.to_numpy(vtx_z_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(brem_vtx_z_found, brem_vtx_x_found, density=False, bins=200,  cmap=plt.cm.jet, cmin=1)\n",
    "ax0.set_xlabel(\"z\")\n",
    "ax0.set_ylabel(\"x\")\n",
    "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
    "\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(brem_vtx_z_lost, brem_vtx_x_lost, density=False, bins=200, cmap=plt.cm.jet, cmin=1)\n",
    "ax1.set_xlabel(\"z\")\n",
    "ax1.set_ylabel(\"x\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
    "\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "vertices of lost e photons are slightly more concentrated around the beampipe, since electrons interact more with matter of the setup there\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at singular event and track\n",
    "\n",
    "def cubic_fit(x, a, b, c, d):\n",
    "    return (a + b*x + c*x**2 + d*x**3)\n",
    "\n",
    "popt,pcov = curve_fit(cubic_fit, vtx_z_fit, vtx_x_fit)\n",
    "popt\n",
    "\n",
    "z_coord = np.linspace(vtx_z_fit[0],12000,1000)\n",
    "fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "\n",
    "plt.plot(z_coord, fit, \"-\", label=\"fit\")\n",
    "plt.plot(vtx_z_fit, vtx_x_fit, \".\", label=\"data\")\n",
    "plt.ylim(-4300,4300)\n",
    "plt.xlabel(\"z [mm]\")\n",
    "plt.ylabel(\"x [mm]\")\n",
    "plt.title(\"fit track of brem vertices from single signal\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env1",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}