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{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10522" ] }, "execution_count": 2, "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": 3, "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": 4, "metadata": {}, "outputs": [], "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)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cut_brem_found = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem_f, axis=0)):\n", " cut_brem_found.begin_record()\n", " cut_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n", " \n", " cut_brem_found.field(\"brem_photons_pe\")\n", " cut_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", " cut_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n", " \n", " #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " cut_brem_found.end_list()\n", " \n", " cut_brem_found.field(\"brem_vtx_z\")\n", " cut_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", " cut_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " cut_brem_found.end_list()\n", " \n", "\n", " \n", " cut_brem_found.end_record()\n", "\n", "cut_brem_found = ak.Array(cut_brem_found)\n", "\n", "\n", "\n", "cut_brem_lost = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem_l, axis=0)):\n", " cut_brem_lost.begin_record()\n", " cut_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n", " \n", " \n", " cut_brem_lost.field(\"brem_photons_pe\")\n", " cut_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", " cut_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n", " \n", " #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " cut_brem_lost.end_list()\n", " \n", " cut_brem_lost.field(\"brem_vtx_z\")\n", " cut_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", " cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n", " cut_brem_lost.end_list()\n", " \n", " cut_brem_lost.end_record()\n", "\n", "cut_brem_lost = ak.Array(cut_brem_lost)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 9.36e+03,\n", " brem_photons_pe: [2.47e+03, 170, 224, 388, 3.23e+03, 809, 172, 224],\n", " brem_vtx_z: [400, 501, 638, 667, 677, 709, 8.58e+03, 9.28e+03]}\n", "---------------------------------------------------------------------\n", "type: {\n", " energy: float64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Record {energy: 9.36e+03, ...} type='{energy: float64, brem_photons_pe: va...'>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#data in cut_brem_found and cut_brem_lost\n", "\n", "cut_length_found = ak.num(cut_brem_found[\"brem_photons_pe\"],axis=-1)\n", "cut_length_lost = ak.num(cut_brem_lost[\"brem_photons_pe\"], axis=-1)\n", "\n", "cut_brem_found[1]\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cut_length_found[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split in Upstream and Downstream Events and analyse separately" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#try to find a split between energy lost before and after the magnet (z~5000mm)\n", "\n", "upstream_found = ak.ArrayBuilder()\n", "downstream_found = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(cut_brem_found, axis=0)):\n", " upstream_found.begin_record()\n", " upstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n", " \n", " downstream_found.begin_record()\n", " downstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n", " \n", " upstream_found.field(\"brem_photons_pe\")\n", " downstream_found.field(\"brem_photons_pe\")\n", " upstream_found.begin_list()\n", " downstream_found.begin_list()\n", " for jentry in range(cut_length_found[itr]):\n", " if (cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000):\n", " if cut_brem_found[itr, \"brem_vtx_z\", jentry]<=9500:\n", " downstream_found.real(cut_brem_found[itr,\"brem_photons_pe\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_found.real(cut_brem_found[itr,\"brem_photons_pe\", jentry]) \n", " upstream_found.end_list()\n", " downstream_found.end_list()\n", " \n", " upstream_found.field(\"brem_vtx_z\")\n", " downstream_found.field(\"brem_vtx_z\")\n", " upstream_found.begin_list()\n", " downstream_found.begin_list()\n", " for jentry in range(cut_length_found[itr]):\n", " if cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000:\n", " if cut_brem_found[itr,\"brem_vtx_z\",jentry]<=9500:\n", " downstream_found.real(cut_brem_found[itr,\"brem_vtx_z\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_found.real(cut_brem_found[itr, \"brem_vtx_z\",jentry])\n", " upstream_found.end_list()\n", " downstream_found.end_list()\n", " upstream_found.end_record()\n", " downstream_found.end_record()\n", " \n", "\n", "upstream_found = ak.Array(upstream_found)\n", "downstream_found = ak.Array(downstream_found)\n", "\n", "\n", "upstream_lost = ak.ArrayBuilder()\n", "downstream_lost = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(cut_brem_lost, axis=0)):\n", " upstream_lost.begin_record()\n", " upstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n", " \n", " downstream_lost.begin_record()\n", " downstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n", " \n", " upstream_lost.field(\"brem_photons_pe\")\n", " downstream_lost.field(\"brem_photons_pe\")\n", " upstream_lost.begin_list()\n", " downstream_lost.begin_list()\n", " for jentry in range(cut_length_lost[itr]):\n", " if (cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000):\n", " if cut_brem_lost[itr, \"brem_vtx_z\", jentry]<=9500:\n", " downstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\", jentry]) \n", " upstream_lost.end_list()\n", " downstream_lost.end_list()\n", " \n", " upstream_lost.field(\"brem_vtx_z\")\n", " downstream_lost.field(\"brem_vtx_z\")\n", " upstream_lost.begin_list()\n", " downstream_lost.begin_list()\n", " for jentry in range(cut_length_lost[itr]):\n", " if cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000:\n", " if cut_brem_lost[itr,\"brem_vtx_z\",jentry]<=9500:\n", " downstream_lost.real(cut_brem_lost[itr,\"brem_vtx_z\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_lost.real(cut_brem_lost[itr, \"brem_vtx_z\",jentry])\n", " upstream_lost.end_list()\n", " downstream_lost.end_list()\n", " upstream_lost.end_record()\n", " downstream_lost.end_record()\n", " \n", "\n", "upstream_lost = ak.Array(upstream_lost)\n", "downstream_lost = ak.Array(downstream_lost)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 4.62e+04,\n", " brem_photons_pe: [3.26e+03, 4.45e+03, 178, 1.45e+04, 1.1e+03, 3.79e+03],\n", " brem_vtx_z: [162, 187, 387, 487, 1.34e+03, 2.32e+03]}\n", "-------------------------------------------------------------------------\n", "type: {\n", " energy: float64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Record {energy: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upstream_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "upstream: cutoff energy = 350MeV, sample size: 1562\n", "eff = 0.9181 +/- 0.007\n" ] } ], "source": [ "#plot efficiency against cutoff energy \n", "up_efficiencies = []\n", "up_deff = []\n", "\n", "\n", "for cutoff_energy in range(0,10050,200):\n", "\tup_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\tup_nobrem_l = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\t\n", "\tif ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0)==0:\n", "\t\tup_efficiencies.append(0)\n", "\t\tup_deff.append(0)\n", "\t\tcontinue\n", "\n", "\teff = t_eff(up_nobrem_f,up_nobrem_l)\n", "\tdeff = eff_err(up_nobrem_f,up_nobrem_l)\n", "\tup_efficiencies.append(eff)\n", "\tup_deff.append(deff)\n", "\n", "\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n", "\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(up_nobrem_f, up_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", "up_nobrem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "up_nobrem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\n", "print(\"\\nupstream: cutoff energy = 350MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n", "print(\"eff = \",np.round(t_eff(up_nobrem_found, up_nobrem_lost),4), \"+/-\", np.round(eff_err(up_nobrem_found, up_nobrem_lost),3))\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nobrem_vertices\n", "upstream: cutoff energy = 350MeV, sample size: 1562\n", "eff = 0.9181 +/- 0.007\n", "\n", "downstream: cutoff energy = 350MeV, sample size: 5131\n", "eff = 0.8864 +/- 0.004\n" ] } ], "source": [ "down_efficiencies = []\n", "down_deff = []\n", "\n", "for cutoff_energy in range(0,10050,200):\n", "\tdown_nobrem_f = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\tdown_nobrem_l = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\n", "\tif ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0)==0:\n", "\t\tdown_efficiencies.append(0)\n", "\t\tdown_deff.append(0)\n", "\t\tcontinue\n", "\teff = t_eff(down_nobrem_f,down_nobrem_l)\n", "\tdeff = eff_err(down_nobrem_f,down_nobrem_l)\n", "\tdown_efficiencies.append(eff)\n", "\tdown_deff.append(deff)\n", "\n", "\n", "\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n", "\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(down_nobrem_f, down_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", "down_nobrem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "down_nobrem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n", "\n", "\n", "print(\"nobrem_vertices\\nupstream: cutoff energy = 350MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n", "print(\"eff = \",np.round(t_eff(up_nobrem_found, up_nobrem_lost),4), \"+/-\", np.round(eff_err(up_nobrem_found, up_nobrem_lost),3))\n", "\n", "print(\"\\ndownstream: cutoff energy = 350MeV, sample size:\",ak.num(down_nobrem_found,axis=0)+ak.num(down_nobrem_lost,axis=0))\n", "print(\"eff = \",np.round(t_eff(down_nobrem_found, down_nobrem_lost),4), \"+/-\", np.round(eff_err(down_nobrem_found, down_nobrem_lost),3))\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x600 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot efficiencies wrt cutoff energy\n", "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n", "x_ = np.arange(0,10050,step=200)\n", "\n", "ax[0].errorbar(x_,up_efficiencies,yerr=up_deff, ls=\"\", capsize=1,fmt=\".\")\n", "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.8,1.0])\n", "ax[0].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n", "ax[0].set_xticks(np.arange(0,10100,step=200),minor=True)\n", "ax[0].grid()\n", "\n", "ax[1].errorbar(x_,down_efficiencies,yerr=down_deff, ls=\"\", capsize=1,fmt=\".\")\n", "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.8,1.0])\n", "ax[1].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n", "ax[1].set_xticks(np.arange(0,10100,step=200),minor=True)\n", "ax[1].grid(True)\n", "\n", "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "brem vertices\n", "upstream eff = 0.851 +/- 0.004\n", "downstream eff = 0.836 +/- 0.005\n" ] } ], "source": [ "cutoff_energy=350\n", "#possibly: instead of checking if any photons exceed the cutoff, use the sum of all photon energies to separate nobrem and brem\n", "\n", "upstream_brem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "up_energy_found = ak.to_numpy(upstream_brem_found[\"energy\"])\n", "up_eph_found = ak.to_numpy(ak.sum(upstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "up_residual_found = up_energy_found - up_eph_found\n", "up_energyloss_found = up_eph_found/up_energy_found\n", "\n", "\n", "upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "up_energy_lost = ak.to_numpy(upstream_brem_lost[\"energy\"])\n", "up_eph_lost = ak.to_numpy(ak.sum(upstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "up_residual_lost = up_energy_lost - up_eph_lost\n", "up_energyloss_lost = up_eph_lost/up_energy_lost\n", "\n", "\n", "print(\"brem vertices\\nupstream eff = \", np.round(t_eff(upstream_brem_found,upstream_brem_lost),3), \"+/-\", np.round(eff_err(upstream_brem_found, upstream_brem_lost),3))\n", "\n", "\n", "downstream_brem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "down_energy_found = ak.to_numpy(downstream_brem_found[\"energy\"])\n", "down_eph_found = ak.to_numpy(ak.sum(downstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "down_residual_found = down_energy_found - down_eph_found\n", "down_energyloss_found = down_eph_found/down_energy_found\n", "\n", "\n", "downstream_brem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "down_energy_lost = ak.to_numpy(downstream_brem_lost[\"energy\"])\n", "down_eph_lost = ak.to_numpy(ak.sum(downstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "down_residual_lost = down_energy_lost - down_eph_lost\n", "down_energyloss_lost = down_eph_lost/down_energy_lost\n", "\n", "\n", "print(\"downstream eff = \", np.round(t_eff(downstream_brem_found,downstream_brem_lost),3), \"+/-\", np.round(eff_err(downstream_brem_found, downstream_brem_lost),3))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upstream:\n", "mean energyloss relative to initial energy (found): 0.33078325542598164\n", "mean energyloss relative to initial energy (lost): 0.5708618852236069\n", "downstream:\n", "mean energyloss relative to initial energy (found): 0.19104090843883118\n", "mean energyloss relative to initial energy (lost): 0.3051594568487781\n" ] } ], "source": [ "print(\"upstream:\\nmean energyloss relative to initial energy (found): \",ak.mean(up_energyloss_found))\n", "print(\"mean energyloss relative to initial energy (lost): \", ak.mean(up_energyloss_lost))\n", "\n", "print(\"downstream:\\nmean energyloss relative to initial energy (found): \",ak.mean(down_energyloss_found))\n", "print(\"mean energyloss relative to initial energy (lost): \", ak.mean(down_energyloss_lost))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x600 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#in abhängigkeit von der energie der elektronen\n", "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n", "\n", "\n", "ax[0].hist(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "ax[0].hist(up_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "ax[0].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n", "ax[0].set_yticks(np.arange(0,11,1), minor=True)\n", "ax[0].set_xlabel(r\"$E_\\gamma/E_0$\")\n", "ax[0].set_ylabel(\"counts (normed)\")\n", "ax[0].set_title(\"Upstream\")\n", "ax[0].legend()\n", "ax[0].grid()\n", "\n", "ax[1].hist(down_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "ax[1].hist(down_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "ax[1].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n", "ax[1].set_yticks(np.arange(0,11,1), minor=True)\n", "ax[1].set_xlabel(r\"$E_\\gamma/E_0$\")\n", "ax[1].set_ylabel(\"counts (normed)\")\n", "ax[1].set_title(\"Downstream\")\n", "ax[1].legend()\n", "ax[1].grid()\n", "\n", "\"\"\"\n", "most electrons lose little energy relative to their initial energy downstream\n", "\"\"\"\n", "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "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", "#upstream\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(up_energyloss_found, up_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\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(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#downstream\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(down_energyloss_found, down_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\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(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "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", "#upstream\n", "nbins=60\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(up_energyloss_found, up_residual_found, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\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\"$E_0-E_\\gamma$\")\n", "ax0.set_title(\"found energyloss wrt residual electron energy\")\n", "\n", "a1=ax1.hist2d(up_energyloss_lost, up_residual_lost, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \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\"$E_0-E_\\gamma$\")\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, Upstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#downstream\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(down_energyloss_found, down_residual_found, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\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\"$E_0-E_\\gamma$\")\n", "ax0.set_title(\"found energyloss wrt residual electron energy\")\n", "\n", "a1=ax1.hist2d(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \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\"$E_0-E_\\gamma$\")\n", "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n", "\n", "fig.colorbar(a0[3],ax=ax1)\n", "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "env1", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2}
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