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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", "from scipy.optimize import curve_fit\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "\n", "\n", "#selektiere nur elektronen von D*0->D0ee und nur solche mit einem momentum von unter 5 GeV \n", "allcolumns = file.arrays()\n", "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p < 5e3)] #D: 2591\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p < 5e3)] #D: 1908\n", "\n", "#ak.num(lost, axis=0)\n", "ak.num(allcolumns[(allcolumns.fromPairProd) & (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)],axis=0)\n", "#found[0]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff all = 0.5759057568348522 +/- 0.007367987865301135\n" ] } ], "source": [ "def t_eff(found, lost, axis = 0):\n", " sel = ak.num(found, axis=axis)\n", " des = ak.num(lost, axis=axis)\n", " return sel/(sel + des)\n", "\n", "def eff_err(found, lost):\n", " n_f = ak.num(found, axis=0)\n", " n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n", " return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n", "\n", "\n", "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7960893854748603" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "#statistics\n", "\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 nicht besonders gut, aber trotzdem besser als\n", "für alle elektronen.\n", "Auch hier handelt es sich um eine recht geringe sample size (~350)\n", "\"\"\"\n", "\n", "t_eff(nobrem_found, nobrem_lost)\n", "#ak.num(nobrem_lost, axis=0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 2.04e+03,\n", " photon_length: 4,\n", " brem_photons_pe: [519, 484, 173, 466],\n", " brem_vtx_z: [9.84e+03, 9.84e+03, 9.85e+03, 9.85e+03]}\n", "------------------------------------------------------\n", "type: {\n", " energy: float64,\n", " photon_length: int64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Record {energy: 2.04e+03, ...} type='{energy: float64, photon_length: int6...'>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#try excluding all photons that originate from a vtx @ z>9500mm\n", "#ignore all brem vertices @ z>9500mm \n", "\n", "#found\n", "\n", "brem_e_f = found[\"brem_photons_pe\"]\n", "brem_z_f = found[\"brem_vtx_z\"]\n", "e_f = found[\"energy\"]\n", "length_f = found[\"brem_vtx_z_length\"]\n", "\n", "brem_f = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(found,axis=0)):\n", " brem_f.begin_record()\n", " #[:,\"energy\"] energy\n", " brem_f.field(\"energy\").append(e_f[itr])\n", " #[:,\"photon_length\"] number of vertices\n", " brem_f.field(\"photon_length\").integer(length_f[itr])\n", " #[:,\"brem_photons_pe\",:] photon energy \n", " brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n", " #[:,\"brem_vtx_z\",:] brem vtx z\n", " brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n", " brem_f.end_record()\n", "\n", "brem_f = ak.Array(brem_f)\n", "\n", "#lost\n", "\n", "brem_e_l = lost[\"brem_photons_pe\"]\n", "brem_z_l = lost[\"brem_vtx_z\"]\n", "e_l = lost[\"energy\"]\n", "length_l = lost[\"brem_vtx_z_length\"]\n", "\n", "brem_l = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(lost,axis=0)):\n", " brem_l.begin_record()\n", " #[:,\"energy\"] energy\n", " brem_l.field(\"energy\").append(e_l[itr])\n", " #[:,\"photon_length\"] number of vertices\n", " brem_l.field(\"photon_length\").integer(length_l[itr])\n", " #[:,\"brem_photons_pe\",:] photon energy \n", " brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n", " #[:,\"brem_vtx_z\",:] brem vtx z\n", " brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n", " brem_l.end_record()\n", "\n", "brem_l = ak.Array(brem_l)\n", "\n", "\n", "\n", "\n", "brem_f[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "acc_brem_found = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem_f, axis=0)):\n", " acc_brem_found.begin_record()\n", " acc_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n", " \n", " acc_brem_found.field(\"brem_photons_pe\")\n", " acc_brem_found.begin_list()\n", " for jentry in range(brem_f[itr, \"photon_length\"]):\n", " if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n", " \n", " #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_found.end_list()\n", " \n", " acc_brem_found.field(\"brem_vtx_z\")\n", " acc_brem_found.begin_list()\n", " for jentry in range(brem_f[itr, \"photon_length\"]):\n", " if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_found.end_list()\n", " \n", "\n", " \n", " acc_brem_found.end_record()\n", "\n", "acc_brem_found = ak.Array(acc_brem_found)\n", "\n", "\n", "\n", "acc_brem_lost = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem_l, axis=0)):\n", " acc_brem_lost.begin_record()\n", " acc_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n", " \n", " acc_brem_lost.field(\"brem_photons_pe\")\n", " acc_brem_lost.begin_list()\n", " for jentry in range(brem_l[itr, \"photon_length\"]):\n", " if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n", " \n", " #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_lost.end_list()\n", " \n", " acc_brem_lost.field(\"brem_vtx_z\")\n", " acc_brem_lost.begin_list()\n", " for jentry in range(brem_l[itr, \"photon_length\"]):\n", " if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_lost.end_list()\n", " \n", " acc_brem_lost.end_record()\n", "\n", "acc_brem_lost = ak.Array(acc_brem_lost)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "cutoff = 50 MeV, sample size: 789\n", "eff = 0.7427122940430925 +/- 0.0156\n", "\n", "cutoff = 100 MeV, sample size: 789\n", "eff = 0.7427122940430925 +/- 0.0156\n", "\n", "cutoff = 150 MeV, sample size: 1025\n", "eff = 0.7346341463414634 +/- 0.0138\n", "\n", "cutoff = 200 MeV, sample size: 1202\n", "eff = 0.7346089850249584 +/- 0.0127\n", "\n", "cutoff = 250 MeV, sample size: 1337\n", "eff = 0.7270007479431563 +/- 0.0122\n", "\n", "cutoff = 300 MeV, sample size: 1474\n", "eff = 0.7164179104477612 +/- 0.0117\n", "\n", "cutoff = 350 MeV, sample size: 1599\n", "eff = 0.7148217636022514 +/- 0.0113\n", "\n", "cutoff = 400 MeV, sample size: 1721\n", "eff = 0.7094712376525276 +/- 0.0109\n", "\n", "cutoff = 450 MeV, sample size: 1854\n", "eff = 0.7076591154261057 +/- 0.0106\n", "\n", "cutoff = 500 MeV, sample size: 1966\n", "eff = 0.7004069175991862 +/- 0.0103\n", "\n", "cutoff = 550 MeV, sample size: 2065\n", "eff = 0.6968523002421307 +/- 0.0101\n", "\n", "cutoff = 600 MeV, sample size: 2152\n", "eff = 0.6909851301115242 +/- 0.01\n", "\n", "cutoff = 650 MeV, sample size: 2252\n", "eff = 0.6873889875666075 +/- 0.0098\n", "\n", "cutoff = 700 MeV, sample size: 2346\n", "eff = 0.6837169650468883 +/- 0.0096\n", "\n", "cutoff = 750 MeV, sample size: 2441\n", "eff = 0.6780008193363376 +/- 0.0095\n", "\n", "cutoff = 800 MeV, sample size: 2534\n", "eff = 0.6764009471191792 +/- 0.0093\n", "\n", "cutoff = 850 MeV, sample size: 2624\n", "eff = 0.6714939024390244 +/- 0.0092\n", "\n", "cutoff = 900 MeV, sample size: 2707\n", "eff = 0.6693756926486886 +/- 0.009\n", "\n", "cutoff = 950 MeV, sample size: 2785\n", "eff = 0.6660682226211849 +/- 0.0089\n", "\n", "cutoff = 1000 MeV, sample size: 2839\n", "eff = 0.6657273687918281 +/- 0.0089\n", "\n", "cutoff energy = 350MeV, sample size: 1599\n", "eff = 0.7148 +/- 0.0113\n" ] } ], "source": [ "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n", "efficiencies_found = []\n", "\n", "\n", "\n", "for cutoff_energy in range(50,1050,50):\n", "\tnobrem_f = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\tnobrem_l = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\n", "\tif ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0)==0:\n", "\t\tcontinue\n", "\teff = t_eff(nobrem_f,nobrem_l)\n", "\tefficiencies_found.append(eff)\n", "\n", "\tprint(\"\\ncutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n", "\tprint(\"eff = \",eff, \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n", "\n", "\"\"\"\n", "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n", "\"\"\"\n", "cutoff_energy = 350.0 #MeV\n", "\n", "\"\"\"\n", "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n", "\"\"\"\n", "nobrem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "nobrem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\n", "print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n", "print(\"eff = \",np.round(t_eff(nobrem_found, nobrem_lost),4), \"+/-\", np.round(eff_err(nobrem_found, nobrem_lost),4))" ] }, { "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": [ "x_ = np.arange(50,1050,step=50)\n", "plt.scatter(x_,efficiencies_found)\n", "plt.xlabel(\"cutoff energy [MeV]\")\n", "plt.ylabel(r\"$\\epsilon$\")\n", "plt.title(r'$D^\\ast\\rightarrow Dee$, $p<5$GeV, photons w/ brem_vtx_z$<9500$mm')\n", "plt.ylim([0.5,0.95])\n", "plt.yticks(np.arange(0.5,0.96,step=0.01),minor=True)\n", "plt.xticks(np.arange(0,1100,step=50),minor=True)\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff = 0.4993 +/- 0.0093\n" ] }, { "data": { "text/html": [ "<pre>[{energy: 4.19e+03, brem_photons_pe: [1e+03, ..., 158], brem_vtx_z: [...]},\n", " {energy: 4.12e+03, brem_photons_pe: [278, 468], brem_vtx_z: [...]},\n", " {energy: 4.53e+03, brem_photons_pe: [445], brem_vtx_z: [9.33e+03]},\n", " {energy: 4.22e+03, brem_photons_pe: [426, ..., 207], brem_vtx_z: [...]},\n", " {energy: 2.54e+03, brem_photons_pe: [362, ...], brem_vtx_z: [...]},\n", " {energy: 3.59e+03, brem_photons_pe: [715], brem_vtx_z: [2.59e+03]},\n", " {energy: 3.78e+03, brem_photons_pe: [198, ..., 180], brem_vtx_z: [...]},\n", " {energy: 3.07e+03, brem_photons_pe: [1.26e+03, ...], brem_vtx_z: [...]},\n", " {energy: 3.89e+03, brem_photons_pe: [486], brem_vtx_z: [5.2e+03]},\n", " {energy: 4.66e+03, brem_photons_pe: [906, ..., 538], brem_vtx_z: [...]},\n", " ...,\n", " {energy: 3.08e+03, brem_photons_pe: [575, 337], brem_vtx_z: [...]},\n", " {energy: 2.39e+03, brem_photons_pe: [163, 971], brem_vtx_z: [...]},\n", " {energy: 2.57e+03, brem_photons_pe: [184, 798], brem_vtx_z: [...]},\n", " {energy: 2.19e+03, brem_photons_pe: [161, 337], brem_vtx_z: [...]},\n", " {energy: 3.62e+03, brem_photons_pe: [1.19e+03], brem_vtx_z: [3.98e+03]},\n", " {energy: 3.96e+03, brem_photons_pe: [135, ...], brem_vtx_z: [...]},\n", " {energy: 3.48e+03, brem_photons_pe: [3.17e+03], brem_vtx_z: [8.58e+03]},\n", " {energy: 3.19e+03, brem_photons_pe: [240, ...], brem_vtx_z: [...]},\n", " {energy: 3.6e+03, brem_photons_pe: [142, ...], brem_vtx_z: [...]}]\n", "---------------------------------------------------------------------------\n", "type: 1452 * {\n", " energy: float64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Array [{energy: 4.19e+03, ...}, ..., {...}] type='1452 * {energy: float64,...'>" ] }, "execution_count": 9, "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", "\n", "cutoff_energy=350\n", "\n", "brem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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", "residual_found = energy_found - eph_found\n", "energyloss_found = eph_found/energy_found\n", "\n", "brem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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", "residual_lost = energy_lost - eph_lost\n", "energyloss_lost = eph_lost/energy_lost\n", "\n", "print(\"eff = \", np.round(t_eff(brem_found,brem_lost),4), \"+/-\", np.round(eff_err(brem_found, brem_lost),4))\n", "brem_lost" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean energyloss relative to initial energy (found): 0.3135\n", "mean energyloss relative to initial energy (lost): 0.4443\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): \", np.round(mean_energyloss_found,4))\n", "print(\"mean energyloss relative to initial energy (lost): \", np.round(mean_energyloss_lost,4))" ] }, { "cell_type": "code", "execution_count": 11, "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=100, 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(0,1.1,0.1), minor=True,)\n", "plt.yticks(np.arange(0,5.5,0.5), minor=True)\n", "plt.xlabel(r\"$E_\\gamma/E_0$\")\n", "plt.ylabel(\"counts (normed)\")\n", "plt.title(r'$D^\\ast\\rightarrow Dee$, $p<5$GeV, photons w/ brem_vtx_z$<9500$mm')\n", "plt.legend()\n", "plt.grid(alpha=0.5)\n", "\n", "\"\"\"\n", "found: elektronen verlieren durchschnittlich 0.44 ihrer anfangsenergie durch bremsstrahlung\n", "lost: elektronen verlieren durchschnittlich 0.59 ihrer anfangsenergie durch bremsstrahlung\n", "\n", "-> lost electrons lose slightly more energy than found electrons. This is however nowhere near as extreme as for the B decay\n", "\"\"\"\n", "\n", "#check if pt near 1 have small eta; perhaps exclude pt with eta outside range; or 2d plot\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#energyloss in abh von der energie der elektronen\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,50), np.linspace(0,6e3,50)), cmap=plt.cm.jet, cmin=1, vmax=8)\n", "ax0.set_ylim(0,6e3)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0$\")\n", "ax0.set_title(\"found energyloss wrt electron energy\")\n", "\n", "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,6e3,50)), cmap=plt.cm.jet, cmin=1, vmax=8)\n", "ax1.set_ylim(0,6e3)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0$\")\n", "ax1.set_title(\"lost energyloss wrt electron energy\")\n", "\n", "fig.colorbar(a1[3],ax=ax1)\n", "fig.suptitle(r\"$D^\\ast\\rightarrow D ee$, $p<5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "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": 14, "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": 15, "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": 16, "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=200, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n", "ax2.hist(scifi_fitpars_lost[:,2], bins=1000, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n", "ax2.set_xlim([-4e-5,4e-5])\n", "ax2.set_xticks(np.arange(-4e-5,4.5e-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=200, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n", "ax3.hist(scifi_fitpars_lost[:,3], bins=1000, 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": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found\n", "a = 18.04180550457298\n", "b = 0.0057651944487250376\n", "c = 9.480277789440454e-08\n", "d = -4.452015498411874e-11\n", "lost\n", "a = -35.48371609662243\n", "b = -0.010382326385451382\n", "c = -6.208301288913938e-07\n", "d = 9.580933791481267e-11\n" ] } ], "source": [ "print(\"found\")\n", "print(\"a = \", str(np.mean(scifi_fitpars_found[:,0])))\n", "print(\"b = \", str(np.mean(scifi_fitpars_found[:,1])))\n", "print(\"c = \", str(np.mean(scifi_fitpars_found[:,2])))\n", "print(\"d = \", str(np.mean(scifi_fitpars_found[:,3])))\n", "\n", "print(\"lost\")\n", "print(\"a = \", str(np.mean(scifi_fitpars_lost[:,0])))\n", "print(\"b = \", str(np.mean(scifi_fitpars_lost[:,1])))\n", "print(\"c = \", str(np.mean(scifi_fitpars_lost[:,2])))\n", "print(\"d = \", str(np.mean(scifi_fitpars_lost[:,3])))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1.5438992626615335e-08" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.min(scifi_fitpars_found[:,3])" ] }, { "cell_type": "code", "execution_count": 19, "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": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found \n", "zmag = 5318.452650242005\n", "lost \n", "zmag = 5425.137423441005\n" ] } ], "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_found = zmag_found[~np.isnan(zmag_found)]\n", "\n", "print(\"found \\nzmag = \", str(np.mean(zmag_found)))\n", "print(\"lost \\nzmag = \", str(np.mean(zmag_lost)))" ] }, { "cell_type": "code", "execution_count": 21, "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(zmag_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "plt.hist(zmag_lost, bins=3000, density=True, alpha=0.5, histtype=\"bar\",color=\"darkorange\", label=\"lost\")\n", "plt.xlabel(\"$z_{mag}$ [mm]\")\n", "plt.ylabel(\"normed\")\n", "plt.title(\"magnet kick position $z_{mag}$ calculated w fitparameters\")\n", "plt.legend()\n", "plt.xticks(np.arange(5100,5805,5), minor=True)\n", "plt.yticks(np.arange(0,0.011,0.001), minor=True)\n", "plt.xlim(5100,5800)\n", "\n", "\"\"\"\n", "wir können definitiv einen unterschied zwischen den z_mag werten, bzw deren verteilungen für lost and found, erkennen\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2591\n", "1908\n", "4499\n", "5767\n" ] } ], "source": [ "both = ak.concatenate([found,lost],axis=0)\n", "print(ak.num(found,axis=0))\n", "print(ak.num(lost,axis=0))\n", "print(ak.num(both,axis=0))\n", "\n", "tracks = allcolumns[(allcolumns.fromSignal) & (allcolumns.isElectron) & (~allcolumns.lost)]\n", "alltracks = allcolumns[(allcolumns.fromSignal) & (allcolumns.isElectron)]\n", "print(ak.num(tracks,axis=0))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#events w/ shared track electrons from found and lost: 35\n", "event_count: [359, 359]\n", "velo idx: [25, 25]\n", "mcp_index: [2926, 2936]\n", "\n", "velo x: [5.89, 5.89]\n", "velo y: [9.81, 9.81]\n", "\n", "velo tx: [0.00824, 0.00824]\n", "velo ty: [0.0135, 0.0135]\n", "percentage of e with shared tracks: 0.7488\n" ] } ], "source": [ "#versuche teilchen von denselben events mit shared tracks zu finden. \n", "#idee: alle teilchen eines events sind durch event_count auffindbar. \n", "a_f_itr = tracks[\"event_count\"].to_numpy()\n", "f_itr = np.unique(a_f_itr)\n", "\n", "shared = ak.ArrayBuilder()\n", "count = 0\n", "\n", "for itr in f_itr:\n", " temp = alltracks[alltracks[\"event_count\"]==itr]\n", " if ak.num(temp,axis=0)>1:\n", " #iterate over cols in temp and append all with duplicate velo_track_idx, such that possibles is array with possible shared tracks particles\n", " #idea: look at one event at a time and at one velo track at a time. if there are at least two e with the same velo_track_idx in the same event,\n", " #concatenate to the array of other shared track particles\n", " _jitr = temp[\"velo_track_idx\"].to_numpy()\n", " jitr = np.unique(_jitr)\n", " for jentry in jitr:\n", " jtem = temp[temp[\"velo_track_idx\"]==jentry]\n", " if ak.num(jtem,axis=0)>1:\n", " shared.append(jtem)\n", " else:\n", " continue\n", " else:\n", " continue\n", "shared = ak.Array(shared)\n", "\n", "idx=0\n", "print(\"#events w/ shared track electrons from found and lost: \",ak.num(shared, axis=0))\n", "\n", "print(\"event_count: \", shared[idx,:,\"event_count\"])\n", "print(\"velo idx: \" ,shared[idx,:,\"velo_track_idx\"])\n", "print(\"mcp_index: \", shared[idx,:,\"mcp_idx\"])\n", "\n", "print(\"\\nvelo x: \" ,shared[idx,:,\"velo_track_x\"])\n", "print(\"velo y: \" ,shared[idx,:,\"velo_track_y\"])\n", "\n", "print(\"\\nvelo tx: \" ,shared[idx,:,\"velo_track_tx\"])\n", "print(\"velo ty: \" ,shared[idx,:,\"velo_track_ty\"])\n", "\n", "\n", "\n", "print(\"percentage of e with shared tracks: \", np.round((ak.num(shared,axis=0)*2)/(ak.num(alltracks,axis=0))*100,4)) #error for percentage" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#velo_track_idx in all events: 217\n", "velo idx: [0, 0, 0]\n", "mcp_index: [1066, 1251, 666]\n", "event_count: [5735, 7049, 7378]\n", "\n", "velo x: [-2.21, 9.45, -18.5]\n", "velo y: [-21.6, -33.4, 17.4]\n", "\n", "velo tx: [-0.0022, 0.0153, -0.0224]\n", "velo ty: [-0.0263, -0.0469, 0.0212]\n" ] } ], "source": [ "#electrons with same velo_track_idx from all events\n", "temp_ = found[\"velo_track_idx\"].to_numpy()\n", "temp = np.unique(temp_)\n", "count=0\n", "psb=ak.ArrayBuilder()\n", "for jentry in temp:\n", " jtem = found[found[\"velo_track_idx\"]==jentry]\n", " if ak.num(jtem,axis=0)>1:\n", " psb.append(jtem)\n", " else:\n", " continue\n", "\n", "psb = ak.Array(psb)\n", "\n", "print(\"#velo_track_idx in all events: \",ak.num(psb,axis=0))\n", "idx = 0\n", "print(\"velo idx: \" ,psb[idx,:,\"velo_track_idx\"])\n", "print(\"mcp_index: \", psb[idx,:,\"mcp_idx\"])\n", "print(\"event_count: \", psb[idx,:,\"event_count\"])\n", "print(\"\\nvelo x: \" ,psb[idx,:,\"velo_track_x\"])\n", "print(\"velo y: \" ,psb[idx,:,\"velo_track_y\"])\n", "\n", "print(\"\\nvelo tx: \" ,psb[idx,:,\"velo_track_tx\"])\n", "print(\"velo ty: \" ,psb[idx,:,\"velo_track_ty\"])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#events w/ shared track electrons from Photon Conversions: 951\n", "shared_idx: 0\n", "event_count: [11, 11]\n", "velo idx: [27, 27]\n", "mcp_index: [1211, 1215]\n", "\n", "velo x: [-20.4, -20.4]\n", "velo y: [-24.4, -24.4]\n", "\n", "velo tx: [-0.0234, -0.0234]\n", "velo ty: [-0.028, -0.028]\n", "percentage of e with shared tracks: 5.59\n" ] } ], "source": [ "#generell: wie viele elektronen von photon conversions teilen sich einen track?\n", "conv = allcolumns[(allcolumns.fromPairProd) & (allcolumns.isElectron) & (~allcolumns.lost)]\n", "\n", "conv_it = conv[\"event_count\"].to_numpy()\n", "conv_itr = np.unique(conv_it)\n", "\n", "cshared = ak.ArrayBuilder()\n", "count = 0\n", "\n", "for itr in conv_itr:\n", " temp = conv[conv[\"event_count\"]==itr]\n", " if ak.num(temp,axis=0)>1:\n", " #iterate over cols in temp and append all with duplicate velo_track_idx, such that possibles is array with possible shared tracks particles\n", " #idea: look at one event at a time and at one velo track at a time. if there are at least two e with the same velo_track_idx in the same event,\n", " #concatenate to the array of other shared track particles\n", " _jitr = temp[\"velo_track_idx\"].to_numpy()\n", " jitr = np.unique(_jitr)\n", " for jentry in jitr:\n", " jtem = temp[temp[\"velo_track_idx\"]==jentry]\n", " if ak.num(jtem,axis=0)>1:\n", " cshared.append(jtem)\n", " else:\n", " continue\n", " else:\n", " continue\n", "cshared = ak.Array(cshared)\n", "\n", "\n", "print(\"#events w/ shared track electrons from Photon Conversions: \",ak.num(cshared, axis=0))\n", "idx = 0\n", "print(\"shared_idx: \", idx)\n", "print(\"event_count: \", cshared[idx,:,\"event_count\"])\n", "print(\"velo idx: \" ,cshared[idx,:,\"velo_track_idx\"])\n", "print(\"mcp_index: \", cshared[idx,:,\"mcp_idx\"])\n", "\n", "print(\"\\nvelo x: \" ,cshared[idx,:,\"velo_track_x\"])\n", "print(\"velo y: \" ,cshared[idx,:,\"velo_track_y\"])\n", "\n", "print(\"\\nvelo tx: \" ,cshared[idx,:,\"velo_track_tx\"])\n", "print(\"velo ty: \" ,cshared[idx,:,\"velo_track_ty\"])\n", "\n", "print(\"percentage of e with shared tracks: \", np.round((ak.num(cshared,axis=0)*2)/(ak.num(conv,axis=0))*100,4))\n", "\n", "#constrain z pos of brem vtx; only interested in conversions in velo z<770\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "34025" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.num(conv,axis=0)" ] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nwas meinst du? velo track sharen? wie überprüfe ich das? versuche ich einfach e teilchen mit identischen velo tracks und slopes zu finden?\\nbenutze ich mother key um elektronen von gleichen events zu finden? meinst du wie viele elektronen vom selben event teilen sich einen track,\\noder wie viele teilchen von unterschiedlichen events haben dieselben tracks?\\n'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "was meinst du? velo track sharen? wie überprüfe ich das? versuche ich einfach e teilchen mit identischen velo tracks und slopes zu finden?\n", "benutze ich mother key um elektronen von gleichen events zu finden? meinst du wie viele elektronen vom selben event teilen sich einen track,\n", "oder wie viele teilchen von unterschiedlichen events haben dieselben tracks?\n", "\"\"\"\n" ] }, { "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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