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{ "cells": [ { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "import uproot\t\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", "from mpl_toolkits.axes_grid1 import ImageGrid\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10522" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\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) + ak.num(lost, axis=0)\n", "#ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff all = 0.8606728758791105 +/- 0.003375885792719708\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": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 4.62e+04,\n", " photon_length: 10,\n", " brem_photons_pe: [3.26e+03, 4.45e+03, 178, ..., 825, 8.99e+03, 3.48e+03],\n", " brem_vtx_z: [162, 187, 387, 487, ..., 9.49e+03, 1.21e+04, 1.21e+04, 1.21e+04]}\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: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>" ] }, "execution_count": 53, "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": 54, "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": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9056" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.num(acc_brem_found,axis=0)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\"\"\"\n", "ph_e = found[\"brem_photons_pe\"]\n", "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n", "ph_e = ph_e[event_cut]\n", "\"\"\"\n", "\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "cutoff energy = 350MeV, sample size: 693\n", "eff = 0.9481 +/- 0.0084\n" ] } ], "source": [ "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n", "#von energie der photonen abmachen\n", "#scan ab welcher energie der photonen die effizienz abfällt\n", "\n", "#abhängigkeit vom ort der emission untersuchen <- noch nicht gemacht\n", "\n", "\n", "\n", "#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n", "\"\"\"\n", "ph_e = acc_brem_found[\"brem_photons_pe\"]\n", "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n", "ph_e = ph_e[event_cut]\n", "\"\"\"\n", "\n", "efficiencies_found = []\n", "deff_found = []\n", "\n", "\n", "for cutoff_energy in range(0,10050,200):\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\tefficiencies_found.append(0)\n", "\t\tdeff_found.append(0)\n", "\t\tcontinue\n", "\t\n", "\teff = t_eff(nobrem_f, nobrem_l)\n", "\tdeff = eff_err(nobrem_f,nobrem_l)\n", "\tefficiencies_found.append(eff)\n", "\tdeff_found.append(deff)\n", "\t#print(\"cutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n", "\t#print(\"eff = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", 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": 80, "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(0,10050,step=200)\n", "\n", "plt.errorbar(x_,efficiencies_found, yerr=deff_found, ls=\"\", capsize=1,fmt=\".\")\t\n", "plt.xlabel(\"cutoff energy [MeV]\")\n", "plt.ylabel(r\"$\\epsilon$\")\n", "plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n", "plt.ylim([0.8,1])\n", "plt.xlim([0,10100])\n", "plt.yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n", "plt.xticks(np.arange(0,10100,step=200),minor=True)\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff = 0.8545 +/- 0.0036\n" ] }, { "data": { "text/html": [ "<pre>[{energy: 2.58e+04, brem_photons_pe: [9.97e+03, ...], brem_vtx_z: [...]},\n", " {energy: 8.03e+04, brem_photons_pe: [4.91e+03, ...], brem_vtx_z: [...]},\n", " {energy: 5.6e+03, brem_photons_pe: [320, ..., 392], brem_vtx_z: [...]},\n", " {energy: 6.36e+03, brem_photons_pe: [273, ...], brem_vtx_z: [...]},\n", " {energy: 4.67e+04, brem_photons_pe: [8.96e+03, ...], brem_vtx_z: [...]},\n", " {energy: 7.16e+04, brem_photons_pe: [544, ..., 142], brem_vtx_z: [...]},\n", " {energy: 5.15e+04, brem_photons_pe: [384, ...], brem_vtx_z: [...]},\n", " {energy: 4.07e+04, brem_photons_pe: [2.7e+04, ...], brem_vtx_z: [...]},\n", " {energy: 2.77e+04, brem_photons_pe: [2.24e+03, ...], brem_vtx_z: [...]},\n", " {energy: 6.4e+04, brem_photons_pe: [686, ..., 796], brem_vtx_z: [...]},\n", " ...,\n", " {energy: 5.59e+03, brem_photons_pe: [901, ...], brem_vtx_z: [...]},\n", " {energy: 2.13e+04, brem_photons_pe: [787, ...], brem_vtx_z: [...]},\n", " {energy: 9.34e+03, brem_photons_pe: [762, ...], brem_vtx_z: [...]},\n", " {energy: 5.08e+04, brem_photons_pe: [711, ...], brem_vtx_z: [...]},\n", " {energy: 6.41e+04, brem_photons_pe: [4.17e+03, ...], brem_vtx_z: [...]},\n", " {energy: 1.01e+04, brem_photons_pe: [220, ..., 156], brem_vtx_z: [...]},\n", " {energy: 1.96e+04, brem_photons_pe: [1.66e+03, ...], brem_vtx_z: [...]},\n", " {energy: 2.98e+04, brem_photons_pe: [8.32e+03, ...], brem_vtx_z: [...]},\n", " {energy: 3.97e+04, brem_photons_pe: [9.36e+03, ...], brem_vtx_z: [...]}]\n", "-------------------------------------------------------------------------\n", "type: 1430 * {\n", " energy: float64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1430 * {energy: float64,...'>" ] }, "execution_count": 25, "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", "#if any photon of an electron has an energy higher the cutoff then it is included\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": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean energyloss relative to initial energy (found): 0.40459562244424735\n", "mean energyloss relative to initial energy (lost): 0.7244570697471976\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": 27, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#in abhängigkeit von der energie der elektronen\n", "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'$E_{ph}/E_0$')\n", "plt.legend()\n", "plt.grid()\n", "\n", "\"\"\"\n", "\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "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", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,70), np.linspace(0,5e4,70)), cmap=plt.cm.jet, cmin=1, vmax=10)\n", "ax0.set_ylim(0,5e4)\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(0,1,70), np.linspace(0,5e4,70)), cmap=plt.cm.jet, cmin=1, vmax=10) \n", "ax1.set_ylim(0,5e4)\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\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "we can see that high energy electrons are often found even though they emit a lot of their energy through bremsstrahlung\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(energyloss_found, residual_found, bins=(np.linspace(0,1,80), np.linspace(0,6e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax0.set_ylim(0,6e4)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n", "ax0.set_title(\"found energyloss wrt residual electron energy\")\n", "\n", "a1=ax1.hist2d(energyloss_lost, residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,6e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15) \n", "ax1.set_ylim(0,6e4)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n", "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n", "\n", "fig.colorbar(a1[3],ax=ax1)\n", "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 30, "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": 31, "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", "#should be fulfilled by all candidates\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", "sf_energy_found = ak.to_numpy(scifi_found[\"energy\"])\n", "sf_eph_found = ak.to_numpy(ak.sum(scifi_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "sf_vtx_type_found = scifi_found[\"all_endvtx_types\"]\n", "\n", "\n", "sf_energy_lost = ak.to_numpy(scifi_lost[\"energy\"])\n", "sf_eph_lost = ak.to_numpy(ak.sum(scifi_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "sf_vtx_type_lost = scifi_lost[\"all_endvtx_types\"]\n", "\n", "\n", "\n", "#ak.num(scifi_found[\"energy\"], axis=0)\n", "#scifi_found.snapshot()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>[101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 0]\n", "------------------\n", "type: 11 * float32</pre>" ], "text/plain": [ "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.num(scifi_found[\"energy\"], axis=0)\n", "scifi_found[\"all_endvtx_types\"][1,:]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "scifi_fitpars_found = ak.ArrayBuilder()\n", "vtx_types_found = ak.ArrayBuilder()\n", "\n", "for i in range(0,ak.num(scifi_found, 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", " #[:,4] -> energy \n", " scifi_fitpars_found.real(sf_energy_found[i])\n", " #[:,5] -> photon energy\n", " scifi_fitpars_found.real(sf_eph_found[i])\n", " scifi_fitpars_found.end_list()\n", " \n", " vtx_types_found.begin_list()\n", " #[:,0] -> endvtx_type\n", " vtx_types_found.extend(sf_vtx_type_found[i,:])\n", " vtx_types_found.end_list()\n", " \n", "\n", "scifi_fitpars_lost = ak.ArrayBuilder()\n", "vtx_types_lost = ak.ArrayBuilder()\n", "\n", "for i in range(0,ak.num(scifi_lost, 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", " #[:,4] -> energy \n", " scifi_fitpars_lost.real(sf_energy_lost[i])\n", " #[:,5] -> photon energy\n", " scifi_fitpars_lost.real(sf_eph_lost[i])\n", " scifi_fitpars_lost.end_list()\n", " \n", " vtx_types_lost.begin_list()\n", " #endvtx_type\n", " vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n", " vtx_types_lost.end_list()\n", " \n", "\n", "\n", "scifi_fitpars_lost = ak.to_numpy(scifi_fitpars_lost)\n", "scifi_fitpars_found = ak.to_numpy(scifi_fitpars_found)\n", "\n", "vtx_types_lost = ak.Array(vtx_types_lost)\n", "vtx_types_found = ak.Array(vtx_types_found)\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>[101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 101,\n", " 0]\n", "------------------\n", "type: 11 * float64</pre>" ], "text/plain": [ "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtx_types_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#b parameter des fits [:,1] hat für lost eine breitere Verteilung. Warum?\n", "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n", "#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n", "\n", "#isolate b parameters for analysis\n", "b_found = scifi_fitpars_found[:,1]\n", "b_lost = scifi_fitpars_lost[:,1]\n", "\n", "brem_energy_found = scifi_fitpars_found[:,5]\n", "brem_energy_lost = scifi_fitpars_lost[:,5]\n", "\n", "\n", "bs_found, vtxs_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n", "bs_found = ak.to_numpy(ak.ravel(bs_found))\n", "vtxs_types_found = ak.to_numpy(ak.ravel(vtxs_types_found))\n", "\n", "bs_lost, vtxs_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n", "bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n", "vtxs_types_lost = ak.to_numpy(ak.ravel(vtxs_types_lost))\n", "\n", "\n", "\n", "\n", "#Erste Annahme ist Bremsstrahlung\n", "\n", "fig, axes = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n", "\n", "\n", "n_bins = (np.linspace(-1,1,100), np.linspace(0,1e5,100))\n", "\n", "h0 = axes[0].hist2d(b_found, brem_energy_found, bins=n_bins, cmap=plt.cm.jet, cmin=1,vmax=15)\n", "axes[0].set_xlim(-1,1)\n", "axes[0].set_ylim(0,1e5)\n", "axes[0].set_xlabel(\"b parameter [mm]\")\n", "axes[0].set_ylabel(r\"$E_{ph}$\")\n", "axes[0].set_title(\"found photon energy wrt b parameter\")\n", "\n", "h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=n_bins, cmap=plt.cm.jet, cmin=1,vmax=15)\n", "axes[1].set_xlim(-1,1)\n", "axes[1].set_ylim(0,1e5)\n", "axes[1].set_xlabel(\"b parameter [mm]\")\n", "axes[1].set_ylabel(r\"$E_{ph}$\")\n", "axes[1].set_title(\"lost photon energy wrt b parameter\")\n", "\n", "fig.colorbar(h1[3], ax=axes[1])\n", "\n", "\"\"\"\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 1800x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n", "\n", "a0=ax[0].hist2d(bs_found, vtx_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n", "ax[0].set_ylim(0,110)\n", "ax[0].set_xlim(-1,1)\n", "ax[0].set_xlabel(\"b\")\n", "ax[0].set_ylabel(\"endvtx id\")\n", "ax[0].set_title(\"found endvtx id wrt b parameter\")\n", "ax[0].set_yticks(np.arange(0,110,1),minor=True)\n", "\n", "a1=ax[1].hist2d(bs_lost, vtx_types_lost, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n", "ax[1].set_ylim(0,110)\n", "ax[1].set_xlim(-1,1)\n", "ax[1].set_xlabel(\"b\")\n", "ax[1].set_ylabel(\"endvtx id\")\n", "ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n", "ax[1].set_yticks(np.arange(0,110,1), minor=True)\n", "\n", "\"\"\"\n", "vtx_id: 101 - Bremsstrahlung\n", "B:\n", "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n", "\"\"\"\n", "fig.colorbar(a0[3], ax=ax, orientation='vertical')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "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_xticks(np.arange(-1,1,0.1),minor=True)\n", "ax1.set_xlabel(\"b\")\n", "ax1.set_ylabel(\"normed\")\n", "ax1.set_title(\"fitparameter b der scifi track\")\n", "ax1.legend()\n", "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n", "#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n", "\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": 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": [] } ], "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": "3.9.12" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2}
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