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{ "cells": [ { "cell_type": "code", "execution_count": 125, "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": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10522" ] }, "execution_count": 126, "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": 127, "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": 128, "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": 129, "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": 130, "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": 130, "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": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 131, "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": 132, "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": 133, "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": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upstream_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "upstream: cutoff energy = 200MeV, sample size: 1279\n", "eff = 0.921 +/- 0.008\n" ] } ], "source": [ "#plot efficiency against cutoff energy \n", "up_efficiencies = []\n", "\n", "\n", "\n", "for cutoff_energy in range(0,1000,50):\n", "\tup_nobrem_f = upstream_found[ak.all(upstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\tup_nobrem_l = upstream_lost[ak.all(upstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\teff = t_eff(up_nobrem_f,up_nobrem_l)\n", "\tup_efficiencies.append(eff)\n", "\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 = 200.0 #MeV\n", "\n", "\"\"\"\n", "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n", "\"\"\"\n", "up_nobrem_found = upstream_found[ak.all(upstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "up_nobrem_lost = upstream_lost[ak.all(upstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\n", "print(\"\\nupstream: cutoff energy = 200MeV, 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": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "downstream: cutoff energy = 200MeV, sample size: 4634\n", "eff = 0.8869 +/- 0.005\n" ] } ], "source": [ "down_efficiencies = []\n", "for cutoff_energy in range(0,1000,50):\n", "\tdown_nobrem_f = downstream_found[ak.all(downstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\tdown_nobrem_l = downstream_lost[ak.all(downstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\teff = t_eff(down_nobrem_f,down_nobrem_l)\n", "\tdown_efficiencies.append(eff)\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 = 200.0 #MeV\n", "\n", "\"\"\"\n", "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n", "\"\"\"\n", "down_nobrem_found = downstream_found[ak.all(downstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "down_nobrem_lost = downstream_lost[ak.all(downstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\n", "print(\"\\ndownstream: cutoff energy = 200MeV, 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": 136, "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.linspace(0,1000,20)\n", "ax[0].scatter(x_,up_efficiencies)\n", "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.85,0.95])\n", "ax[0].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n", "ax[0].grid()\n", "\n", "ax[1].scatter(x_,down_efficiencies)\n", "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.85,0.95])\n", "ax[1].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n", "ax[1].grid()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upstream eff = 0.852 +/- 0.004\n", "downstream eff = 0.84 +/- 0.005\n" ] } ], "source": [ "cutoff_energy=200\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.any(upstream_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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.any(upstream_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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(\"upstream 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.any(downstream_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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.any(downstream_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upstream:\n", "mean energyloss relative to initial energy (found): 0.3207102540525612\n", "mean energyloss relative to initial energy (lost): 0.5602258293743071\n", "downstream:\n", "mean energyloss relative to initial energy (found): 0.17552539358035377\n", "mean energyloss relative to initial energy (lost): 0.2870828762276071\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": 139, "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, only photons w/ brem_vtx_z$<9500$mm\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 140, "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(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax0.set_ylim(0,1.5e5)\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(0,1.5e5,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax1.set_ylim(0,1.5e5)\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": 141, "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(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax0.set_ylim(0,1.5e5)\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(0,1.5e5,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax1.set_ylim(0,1.5e5)\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": 142, "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(0,1.5e5,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\n", "ax0.set_ylim(0,1.5e5)\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(0,1.5e5,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \n", "ax1.set_ylim(0,1.5e5)\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": 143, "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(0,1.5e5,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\n", "ax0.set_ylim(0,1.5e5)\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(0,1.5e5,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \n", "ax1.set_ylim(0,1.5e5)\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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