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{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": 2, "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", "\n", "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n", "allcolumns = file.arrays()\n", "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n", "\n", "#ak.num(found, axis=0)\n", "#ak.count(found, axis=None)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8606728758791105" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def t_eff(found, lost):\n", " sel = found[\"energy\"]\n", " des = lost[\"energy\"]\n", " return ak.count(sel,axis=None)/(ak.count(sel,axis=None)+ak.count(des,axis=None))\n", "\n", "t_eff(found, lost)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.96875" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n", "nobrem_found = found[found[\"brem_photons_pe_length\"]==0]\n", "nobrem_lost = lost[lost[\"brem_photons_pe_length\"]==0]\n", "\n", "\"\"\"\n", "die effizienz mit der wir elektronen finden, die keine bremsstrahlung gemacht haben, ist gut mit 0.9688.\n", "allerdings haben wir hier nur sehr wenige teilchen (<100)\n", "\"\"\"\n", "\n", "t_eff(nobrem_found, nobrem_lost)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8603431839847474" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n", "brem_found = found[found[\"brem_photons_pe_length\"]!=0]\n", "energy_found = ak.to_numpy(brem_found[\"energy\"])\n", "eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "energyloss_found = eph_found/energy_found\n", "\n", "\n", "brem_lost = lost[lost[\"brem_photons_pe_length\"]!=0]\n", "energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n", "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "energyloss_lost = eph_lost/energy_lost\n", "\n", "t_eff(brem_found,brem_lost)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean energyloss relative to initial energy (found): 0.6475128752780828\n", "mean energyloss relative to initial energy (lost): 0.8241268441538472\n" ] } ], "source": [ "mean_energyloss_found = ak.mean(energyloss_found)\n", "mean_energyloss_lost = ak.mean(energyloss_lost)\n", "print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n", "print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_lost)" ] }, { "cell_type": "code", "execution_count": 7, "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(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "plt.xticks(np.arange(0,1,0.1), minor=True,)\n", "plt.yticks(np.arange(0,10,1), minor=True)\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", "plt.grid()\n", "\n", "\"\"\"\n", "found: elektronen verlieren durchschnittlich 0.65 ihrer anfangsenergie durch bremsstrahlung\n", "lost: elektronen verlieren durchschnittlich 0.82 ihrer anfangsenergie durch bremsstrahlung\n", "\n", "-> wir können sofort erkennen, dass verlorene elektronen im schnitt mehr energie durch bremsstrahlung verlieren als gefundene, \n", "aber auch die rate der gefundenen elektronen steigt für raten nahe 1, wenn auch wesentlich schwächer als für verlorene elektronen.\n", "die meisten verlorenen elektronen verlieren >0.8 ihrer anfangsenergie.\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "#ist die shape der teilspur im scifi anders? (koenntest du zum beispiel durch vergleich der verteilungen der fit parameter studieren,\n", "#in meiner thesis findest du das fitmodell -- ist einfach ein polynom dritten grades)\n", "z_ref=8520 #mm\n", "\n", "def scifi_track(z, a, b, c, d):\n", " return a + b*(z-z_ref) + c*(z-z_ref)**2 + d*(z-z_ref)**3\n", "\n", "def z_mag(xv, zv, tx, a, b):\n", " \"\"\" optical centre of the magnet is defined as the intersection between the trajectory tangents before and after the magnet\n", "\n", " Args:\n", " xv (double): velo x track\n", " zv (double): velo z track\n", " tx (double): velo x slope\n", " a (double): ax parameter of track fit\n", " b (double): bx parameter of track fit\n", "\n", " Returns:\n", " double: z_mag\n", " \"\"\"\n", " return (xv-tx*zv-a+b*z_ref)/(b-tx)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "scifi_found = found[found[\"scifi_hit_pos_x_length\"]>3]\n", "scifi_lost = lost[lost[\"scifi_hit_pos_x_length\"]>3]\n", "\n", "scifi_x_found = scifi_found[\"scifi_hit_pos_x\"]\n", "scifi_z_found = scifi_found[\"scifi_hit_pos_z\"]\n", "\n", "tx_found = scifi_found[\"velo_track_tx\"]\n", "\n", "scifi_x_lost = scifi_lost[\"scifi_hit_pos_x\"]\n", "scifi_z_lost = scifi_lost[\"scifi_hit_pos_z\"]\n", "\n", "tx_lost = scifi_lost[\"velo_track_tx\"]\n", "\n", "xv_found = scifi_found[\"velo_track_x\"]\n", "zv_found = scifi_found[\"velo_track_z\"]\n", "\n", "xv_lost = scifi_lost[\"velo_track_x\"]\n", "zv_lost = scifi_lost[\"velo_track_z\"]\n", "\n", "\n", "\n", "#ak.num(scifi_found[\"energy\"], axis=0)\n", "#scifi_found.snapshot()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "#tx_lost.show()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "scifi_fitpars_found = ak.ArrayBuilder()\n", "\n", "for i in range(0,ak.num(scifi_found[\"energy\"], axis=0)):\n", " popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]))\n", " scifi_fitpars_found.begin_list()\n", " scifi_fitpars_found.real(popt[0])\n", " scifi_fitpars_found.real(popt[1])\n", " scifi_fitpars_found.real(popt[2])\n", " scifi_fitpars_found.real(popt[3])\n", " scifi_fitpars_found.end_list()\n", "\n", "scifi_fitpars_lost = ak.ArrayBuilder()\n", "\n", "for i in range(0,ak.num(scifi_lost[\"energy\"], axis=0)):\n", " popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]))\n", " scifi_fitpars_lost.begin_list()\n", " scifi_fitpars_lost.real(popt[0])\n", " scifi_fitpars_lost.real(popt[1])\n", " scifi_fitpars_lost.real(popt[2])\n", " scifi_fitpars_lost.real(popt[3])\n", " scifi_fitpars_lost.end_list()\n", "\n", "\n", "scifi_fitpars_lost = scifi_fitpars_lost.to_numpy()\n", "scifi_fitpars_found = scifi_fitpars_found.to_numpy()\n", "\n", "\n", "\n", "dtx_found = scifi_fitpars_found[:,1] - tx_found\n", "dtx_lost = scifi_fitpars_lost[:,1] - tx_lost\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 1500x1000 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\n", "\n", "ax0.hist(scifi_fitpars_found[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$a_x$ found\")\n", "ax0.hist(scifi_fitpars_lost[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$a_x$ lost\")\n", "ax0.set_xlabel(\"a\")\n", "ax0.set_ylabel(\"normed\")\n", "ax0.set_title(\"fitparameter a der scifi track\")\n", "ax0.legend()\n", "\n", "ax1.hist(scifi_fitpars_found[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$b_x$ found\")\n", "ax1.hist(scifi_fitpars_lost[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$b_x$ lost\")\n", "ax1.set_xlabel(\"b\")\n", "ax1.set_ylabel(\"normed\")\n", "ax1.set_title(\"fitparameter b der scifi track\")\n", "ax1.legend()\n", "\n", "ax2.hist(scifi_fitpars_found[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n", "ax2.hist(scifi_fitpars_lost[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n", "ax2.set_xlim([-3e-5,3e-5])\n", "ax2.set_xticks(np.arange(-3e-5,3.5e-5,1e-5),minor=False)\n", "ax2.set_xlabel(\"c\")\n", "ax2.set_ylabel(\"normed\")\n", "ax2.set_title(\"fitparameter c der scifi track\")\n", "ax2.legend()\n", "\n", "ax3.hist(scifi_fitpars_found[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n", "ax3.hist(scifi_fitpars_lost[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$d_x$ lost\")\n", "ax3.set(xlim=(-5e-8,5e-8))\n", "ax3.text(-4e-8,3e8,\"d negligible <1e-7\")\n", "ax3.set_xlabel(\"d\")\n", "ax3.set_ylabel(\"normed\")\n", "ax3.set_title(\"fitparameter d der scifi track\")\n", "ax3.legend()\n", "\n", "\"\"\"\n", "a_x: virtual hit on the reference plane\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-4.6785491318157854e-07" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.min(scifi_fitpars_found[:,3])" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 1500x600 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(15,6))\n", "\n", "for i in range(0,ak.num(scifi_found[\"energy\"], axis=0)):\n", " z_coord = np.linspace(scifi_z_found[i,0],12000,300)\n", " fit = scifi_track(z_coord, *scifi_fitpars_found[i])\n", " ax0.plot(z_coord, fit, \"-\", lw=0.5)\n", " ax0.errorbar(ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]),fmt=\".\",ms=2)\n", "\n", "#ax0.legend()\n", "ax0.set_xlabel(\"z [mm]\")\n", "ax0.set_ylabel(\"x [mm]\")\n", "ax0.set_title(\"found tracks of scifi hits\")\n", "ax0.set(xlim=(7e3,12000), ylim=(-4000,4000))\n", "ax0.grid()\n", "\n", "for i in range(0,ak.num(scifi_lost[\"energy\"], axis=0)):\n", " z_coord = np.linspace(scifi_z_lost[i,0],12000,300)\n", " fit = scifi_track(z_coord, *scifi_fitpars_lost[i])\n", " ax1.plot(z_coord, fit, \"-\", lw=0.5)\n", " ax1.errorbar(ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]),fmt=\".\",ms=2)\n", "\n", "#ax1.legend()\n", "ax1.set_xlabel(\"z [mm]\")\n", "ax1.set_ylabel(\"x [mm]\")\n", "ax1.set_title(\"lost tracks of scifi hits\")\n", "ax1.set(xlim=(7e3,12000), ylim=(-4000,4000))\n", "ax1.grid()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "#vergleich der zmag werte\n", "zmag_found = z_mag(xv_found, zv_found, tx_found, scifi_fitpars_found[:,0], scifi_fitpars_found[:,1])\n", "zmag_lost = z_mag(xv_lost, zv_lost, tx_lost, scifi_fitpars_lost[:,0], scifi_fitpars_lost[:,1])\n", "zmag_lost = zmag_lost[~np.isnan(zmag_lost)]\n", "#zmag_lost.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": [] }, { "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}
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