Projektpraktikum/electron_lost_found.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 52,
   "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",
    "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",
    "#print(ak.to_numpy(brem_vtx_z_found))\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_found)\n",
    "#vtx_z_fit = ak.to_numpy(vtx_z_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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=300,  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=300, 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",
    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
    "B:\n",
    "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
    "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot singular tracks by fitting brem vertices\n",
    "vtx_z_found = tracked[\"brem_vtx_z\"]\n",
    "vtx_z_found = vtx_z_found[tracked[\"brem_vtx_z_length\"]>3]\n",
    "\n",
    "vtx_x_found = tracked[\"brem_vtx_x\"]\n",
    "vtx_x_found = vtx_x_found[tracked[\"brem_vtx_x_length\"]>3]\n",
    "\n",
    "vtx_z_lost = lost[\"brem_vtx_z\"]\n",
    "vtx_z_lost = vtx_z_lost[lost[\"brem_vtx_z_length\"]>3]\n",
    "\n",
    "vtx_x_lost = lost[\"brem_vtx_x\"]\n",
    "vtx_x_lost = vtx_x_lost[lost[\"brem_vtx_x_length\"]>3]\n",
    "\n",
    "def cubic_fit(x, a, b, c, d):\n",
    "    return (a + b*x + c*x**2 + d*x**3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "for i in range(3,6):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]))\n",
    "    z_coord = np.linspace(vtx_z_found[i,0],12000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax0.plot(z_coord, fit, \"-\", label=\"fit\"+str(i))\n",
    "\n",
    "ax0.legend()\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found tracks of brem vertices from few signals\")\n",
    "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "ax0.grid()\n",
    "\n",
    "for i in range(3,6):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]))\n",
    "    z_coord = np.linspace(vtx_z_lost[i,0],12000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax1.plot(z_coord, fit, \"-\", label=\"fit\"+str(i))\n",
    "\n",
    "ax1.legend()\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost tracks of brem vertices from few signals\")\n",
    "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "ax1.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}