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1064 lines
414 KiB
1064 lines
414 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\t\n",
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"import numpy as np\n",
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"import sys\n",
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"import os\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\n",
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"import itertools\n",
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"import awkward as ak\n",
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"from scipy.optimize import curve_fit\n",
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"from mpl_toolkits.axes_grid1 import ImageGrid\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"10522"
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]
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},
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"execution_count": 51,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
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"\n",
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"#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
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"allcolumns = file.arrays()\n",
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"found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
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"lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
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"\n",
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"ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
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"#ak.count(found, axis=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"eff all = 0.8606728758791105 +/- 0.003375885792719708\n"
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]
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}
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],
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"source": [
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"def t_eff(found, lost, axis = 0):\n",
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" sel = ak.num(found, axis=axis)\n",
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" des = ak.num(lost, axis=axis)\n",
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" return sel/(sel + des)\n",
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"\n",
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"def eff_err(found, lost):\n",
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" n_f = ak.num(found, axis=0)\n",
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" n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
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" return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
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"\n",
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"\n",
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"print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre>{energy: 4.62e+04,\n",
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" photon_length: 10,\n",
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" brem_photons_pe: [3.26e+03, 4.45e+03, 178, ..., 825, 8.99e+03, 3.48e+03],\n",
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" brem_vtx_z: [162, 187, 387, 487, ..., 9.49e+03, 1.21e+04, 1.21e+04, 1.21e+04]}\n",
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"-------------------------------------------------------------------------------\n",
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"type: {\n",
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" energy: float64,\n",
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" photon_length: int64,\n",
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" brem_photons_pe: var * float64,\n",
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" brem_vtx_z: var * float64\n",
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"}</pre>"
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],
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"text/plain": [
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"<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
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]
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},
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"execution_count": 53,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#try excluding all photons that originate from a vtx @ z>9500mm\n",
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"#ignore all brem vertices @ z>9500mm \n",
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"\n",
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"#found\n",
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"\n",
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"brem_e_f = found[\"brem_photons_pe\"]\n",
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"brem_z_f = found[\"brem_vtx_z\"]\n",
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"e_f = found[\"energy\"]\n",
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"length_f = found[\"brem_vtx_z_length\"]\n",
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"\n",
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"brem_f = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(found,axis=0)):\n",
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" brem_f.begin_record()\n",
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" #[:,\"energy\"] energy\n",
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" brem_f.field(\"energy\").append(e_f[itr])\n",
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" #[:,\"photon_length\"] number of vertices\n",
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" brem_f.field(\"photon_length\").integer(length_f[itr])\n",
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" #[:,\"brem_photons_pe\",:] photon energy \n",
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" brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
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" #[:,\"brem_vtx_z\",:] brem vtx z\n",
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" brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
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" brem_f.end_record()\n",
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"\n",
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"brem_f = ak.Array(brem_f)\n",
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"\n",
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"#lost\n",
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"\n",
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"brem_e_l = lost[\"brem_photons_pe\"]\n",
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"brem_z_l = lost[\"brem_vtx_z\"]\n",
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"e_l = lost[\"energy\"]\n",
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"length_l = lost[\"brem_vtx_z_length\"]\n",
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"\n",
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"brem_l = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(lost,axis=0)):\n",
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" brem_l.begin_record()\n",
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" #[:,\"energy\"] energy\n",
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" brem_l.field(\"energy\").append(e_l[itr])\n",
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" #[:,\"photon_length\"] number of vertices\n",
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" brem_l.field(\"photon_length\").integer(length_l[itr])\n",
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" #[:,\"brem_photons_pe\",:] photon energy \n",
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" brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
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" #[:,\"brem_vtx_z\",:] brem vtx z\n",
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" brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
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" brem_l.end_record()\n",
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"\n",
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"brem_l = ak.Array(brem_l)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"brem_f[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [],
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"source": [
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"acc_brem_found = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(brem_f, axis=0)):\n",
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" acc_brem_found.begin_record()\n",
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" acc_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
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" \n",
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" acc_brem_found.field(\"brem_photons_pe\")\n",
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" acc_brem_found.begin_list()\n",
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" for jentry in range(brem_f[itr, \"photon_length\"]):\n",
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" if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" acc_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
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" \n",
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" #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_found.end_list()\n",
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" \n",
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" acc_brem_found.field(\"brem_vtx_z\")\n",
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" acc_brem_found.begin_list()\n",
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" for jentry in range(brem_f[itr, \"photon_length\"]):\n",
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" if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_found.end_list()\n",
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" \n",
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"\n",
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" \n",
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" acc_brem_found.end_record()\n",
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"\n",
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"acc_brem_found = ak.Array(acc_brem_found)\n",
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"\n",
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"\n",
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"\n",
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"acc_brem_lost = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(brem_l, axis=0)):\n",
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" acc_brem_lost.begin_record()\n",
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" acc_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
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" \n",
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" acc_brem_lost.field(\"brem_photons_pe\")\n",
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" acc_brem_lost.begin_list()\n",
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" for jentry in range(brem_l[itr, \"photon_length\"]):\n",
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" if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" acc_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
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" \n",
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" #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_lost.end_list()\n",
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" \n",
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" acc_brem_lost.field(\"brem_vtx_z\")\n",
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" acc_brem_lost.begin_list()\n",
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" for jentry in range(brem_l[itr, \"photon_length\"]):\n",
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" if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" acc_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_lost.end_list()\n",
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" \n",
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" acc_brem_lost.end_record()\n",
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"\n",
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"acc_brem_lost = ak.Array(acc_brem_lost)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"9056"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ak.num(acc_brem_found,axis=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'"
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]
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},
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"execution_count": 56,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"\"\"\"\n",
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"ph_e = found[\"brem_photons_pe\"]\n",
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"event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
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"ph_e = ph_e[event_cut]\n",
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"\"\"\"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"cutoff energy = 350MeV, sample size: 693\n",
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"eff = 0.9481 +/- 0.0084\n"
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]
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}
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],
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"source": [
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"#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
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"#von energie der photonen abmachen\n",
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"#scan ab welcher energie der photonen die effizienz abfällt\n",
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"\n",
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"#abhängigkeit vom ort der emission untersuchen <- noch nicht gemacht\n",
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"\n",
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"\n",
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"\n",
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"#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n",
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"\"\"\"\n",
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"ph_e = acc_brem_found[\"brem_photons_pe\"]\n",
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"event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
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"ph_e = ph_e[event_cut]\n",
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"\"\"\"\n",
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"\n",
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"efficiencies_found = []\n",
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"deff_found = []\n",
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"\n",
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"\n",
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"for cutoff_energy in range(0,10050,200):\n",
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"\tnobrem_f = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
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"\tnobrem_l = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
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"\n",
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"\tif ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0)==0:\n",
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"\t\tefficiencies_found.append(0)\n",
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"\t\tdeff_found.append(0)\n",
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"\t\tcontinue\n",
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"\t\n",
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"\teff = t_eff(nobrem_f, nobrem_l)\n",
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"\tdeff = eff_err(nobrem_f,nobrem_l)\n",
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"\tefficiencies_found.append(eff)\n",
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"\tdeff_found.append(deff)\n",
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"\t#print(\"cutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
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"\t#print(\"eff = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n",
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"\n",
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"\"\"\"\n",
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"we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
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"\"\"\"\n",
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"cutoff_energy = 350.0 #MeV\n",
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"\n",
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"\"\"\"\n",
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"better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
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"\"\"\"\n",
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"nobrem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
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"nobrem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
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"\n",
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"print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
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"print(\"eff = \",np.round(t_eff(nobrem_found, nobrem_lost),4), \"+/-\", np.round(eff_err(nobrem_found, nobrem_lost),4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
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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": {
|
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"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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Xv4wk8/3335vs3HPPPaZ06dLZvp+dVatWGUnmyy+/NMYYM3v2bBMREWGGDRtm2rVr5w5Xq1Yt079/f48ySzKjR4/OFOfdd99ta2ArQ3ZtMGHCBFOiRAmzadMmj+0Z9bFs2TJjjDHPP/+8kWSOHDmSbRpZ3QTY7be5GeA4X2pqqjlz5oypVauWuf/++23FeS67dWBM5ptou/t++eWXRlKmL9jP16BBA4+bvwwZ56SrrrrKY3tSUpIJCwsz11xzjcf23bt3m5CQEI/+NGDAACPJfPDBBx5hr7nmGlOnTp0c87V7924TGBho7r33Xo/tx44dMzExMR4353bTyfhiYcGCBR7hNm3aZCSZ1157zb2tatWqJiAgwPzyyy8eYR988EFjWZbHAIsxxnTu3DnTzfGAAQNM+fLlzenTp93bnnvuOVOiRAmvN8IAAHhz/perdj+j//77byPJTJkyJcf4s7tGyEpertO//fZbj7D169c3nTt3dv+dm+slScblcpnDhw97hO3bt68JDw83Bw8edG9LS0sz9evXz/TFdJs2bUyTJk089r/rrrtMZGSkOXbsmHvb+ddmjzzySJblueuuu4xlWe5rCTvX9Q0bNjTXXnttjmGy8sQTT5iwsDD3l8233Xab6dKli4mLi3P/2OPPP//0uBfJKHNW1+vG5K79jcm+DTp37mwuuugik5yc7LH9nnvuMaGhoe7w3bt3z1T/58uqz4eGhprevXt7hPvmm2+MpDwPcJwrPT3dpKSkmD/++MNIMh9//LHXOM9ntw6yupewu+9TTz2V5Rfs5zp48GC29znZ3Qfavbc15uyxERoaav744w/3tpMnT5ro6GgzdOjQbPNlTO6v0+2kM3ToUFOqVCmPcMb87z4z43o+o95r1qzpMYBljDGXXXaZqVy5ssf1/LFjx0yZMmU87i3T0tJMjRo1TK9evTz279q1q6lZs6bHgCxQnPGIKqAY+uWXX/TXX38pPj5eJUr87zRQqlQpXX/99dqwYUOW06jtaNeunSIiItx/V6hQQeXLl9cff/whSTpx4oQ2bdqk6667TqGhoe5wERER6tGjh9f4161bp8OHD2vAgAFKTU11v9LT09WlSxdt2rQp09Tynj17evwdFxenU6dO6cCBA5KkJUuWyLIs3XLLLR5xxsTEqHHjxpkWS4uKitLVV1/tsW3t2rWKiIhQly5dPLb369cvUxnuuusuSdK0adPc21555RU1atRIV111VbZlX716tSS5p/tm6Nu3r8LDw92P6GrSpImCg4N1xx13aObMmZmm70vS5ZdfriNHjqhfv376+OOPbU+Lbt26tUJDQ7Vq1SpJZ6dZt23bVl26dNG6dev0zz//aM+ePfr111/VoUOHTPtff/31ttLxJqs2WLJkiRo2bKgmTZp4tGPnzp09podfdtllkqQbbrhBH3zwgf7880+v6eW332YnNTVV48ePV/369RUcHKzAwEAFBwfr119/zfTIBzvs1kF+9s2Ymn/33Xfnpchu5/eF9evX6+TJk5n6d+XKlXX11VdnegSdZVmZ6j4uLs59rsnOZ599ptTUVN16660e5QwNDVWbNm0y1ZGddJYsWaLSpUurR48eHnE2adJEMTExmeKMi4tT7dq1PbatXbtWDRs2VP369T22Z3UOue+++3TgwAF9+OGHks4+euH1119Xt27dvD7mAQCA3LL7GR0dHa2aNWtq0qRJeuGFF7R169ZMj1LKrdxep8fExOjyyy/32JbV53ZurpeuvvpqRUVFeWxbu3atrr76apUtW9a9rUSJErrhhhsyleG+++7T999/r2+++UbS2Uc/zZo1SwMGDFCpUqWyLfvq1atVv379TOUZOHCgjDHuewM71/WXX365Pv30Uz3yyCP64osvdPLkyWzTPVf79u118uRJrVu3TtLZxwR37NhRHTp00MqVK93bJGW69s/qej2vzm+DU6dO6fPPP1fv3r1VsmRJj3a85pprdOrUKW3YsEHS2bL/8MMPGjZsmD777DMdPXrUa3rr16/XqVOndPPNN3tsb9WqlapWrZrnchw4cEB33nmnKleurMDAQAUFBbnjy+21f27qID/7fvrpp6pdu3aW93a5cf61v9172wxNmjRRlSpV3H+Hhoaqdu3aXq/9c3udbiedJUuWqF27doqNjfWIs2vXrpLOnh/O1bNnTwUFBbn/PnHihL777jtde+21Cg4Odm8vVapUpvuOEiVK6J577tGSJUu0e/duSdJ///tfLV++XMOGDcv02HCguGKAAyiGDh06JEmqWLFipvdiY2OVnp6upKSkPMVdpkyZTNtCQkLcF9FJSUlKT09XTExMpnBZbTvf/v37JUl9+vRRUFCQx+u5556TMUaHDx/OMU8ZC3pl5Gn//v0yxqhChQqZ4tywYUOmm4Ss6u3QoUOqUKFCpu3Zbbvxxhv15ptvKi0tTdu2bdNXX32le+65J8eyHzp0SIGBgZkWwLMsSzExMe52rVmzplatWqXy5cvr7rvvVs2aNVWzZk2P58zGx8fr3Xff1R9//KHrr79e5cuXV/Pmzd03KtkJDQ1V69at3Tcyn3/+uTp27Ki2bdsqLS1NX331lTuOrC6Cs6q7vMgqnv3792vbtm2Z2jAiIkLGGHc7XnXVVVq0aJH7S+6LLrpIDRs2zPEZxPntt9l54IEH9OSTT+raa6/V4sWL9e2332rTpk1q3Lix7RvPc9mtg/zse/DgQQUEBOSr3FLmNvR2Xsp4P0PJkiU9Bpuks8e2t/UnMs4hl112Waayvv/++5nqyE46+/fv15EjRxQcHJwpzsTERMfPIU2bNtWVV17pfkbykiVLtGvXLq/nEAAA8sLuZ7RlWfr888/VuXNnTZw4UZdcconKlSun4cOH69ixY3lKO7fX6d7uRTLizM31Un4/t3v16qVq1aq5P7dnzJihEydOeP2xyKFDh7Kt84z3JXvX9S+//LIefvhhLVq0SO3atVN0dLSuvfZa/frrrznmIWP9jFWrVum3337Trl273AMc3377rY4fP65Vq1apRo0aql69use+Tl33ZxXXoUOHlJqaqqlTp2Zqx2uuuUaS3O346KOP6vnnn9eGDRvUtWtXlSlTRu3bt9d3332XbXoZdevktX96ero6deqkjz76SA899JA+//xzbdy40T2QkNtr/9zUQX72PXjwoC666KI8lflcWbWhnXvbDHaO7azk9jrd7jlk8eLFmeLLWFPH2zkkKSnJfV47X1bbBg8erLCwML3xxhuSpFdffVVhYWEea0gCxV3ghc4AAN/L+NDet29fpvf++usvlShRwv0LmdDQ0CwX7c3rQmhRUVGyLEuJiYmZ3stq2/kyfiU1depUtWjRIsswWV0UeIvTsix99dVX7sGPc52/LatfSZQpU0YbN27MtD27Mt13332aNWuWPv74Yy1fvty9KHlOypQpo9TUVB08eNDjQtAYo8TERPfMBEm68sordeWVVyotLU3fffedpk6dqhEjRqhChQq66aabJEmDBg3SoEGDdOLECX355ZdKSEhQ9+7dtWPHjhx/mdS+fXuNHj1aGzdu1N69e9WxY0dFRETosssu08qVK/XXX3+pdu3aqly5cqZ9nfqFSVbxlC1bVmFhYdkuNnfuL+x69eqlXr166fTp09qwYYMmTJig/v37q1q1amrZsmWmfXPTbzO+DD//uDn/Il2SZs+erVtvvVXjx4/32P7333+rdOnSWZYjJ7mpg7zuW65cOaWlpSkxMTFfN67nt6G381JOec+NjHj+9a9/5esXeOfHWaZMGS1fvjzL98+d1SZlfw7JGHw5V3bnkOHDh6tv377asmWLXnnlFdWuXVsdO3bMQ+4BAMhZbj6jq1atqnfeeUeStGPHDn3wwQcaM2aMzpw54/5yLjdye51uN87cXC/l93O7RIkSuvvuu/XYY49p8uTJeu2119S+fXvVqVMnx3yWKVMm2zo/P5/eruvDw8M1duxYjR07Vvv373fP5ujRo4f+85//ZJuH4OBgXXHFFVq1apUuuugixcTEqFGjRqpRo4akswtpf/755+revXumfZ38Zfn5cUVFRSkgIEDx8fHZDhRlDLgEBgbqgQce0AMPPKAjR45o1apVeuyxx9S5c2ft2bNHJUuWzLRvRp/P7tr/3Bmz2V37n3+//OOPP+qHH37QjBkzNGDAAPf23377Lbti5yg3dZCffcuVK6e9e/fmKY/nyura3+69bX7k9jrdbpxxcXF65plnsnw/YxAyQ1b917Is2+cQl8ulAQMG6O2339aoUaM0ffp09e/fP0/3i0BRxQAHUAzVqVNHlSpV0ty5czVq1Cj3B+6JEye0YMECtWzZ0n2hV61aNR04cED79+93DxycOXNGn332WZ7SDg8P1+WXX66PPvpIkyZNcl8QHjt2TIsXL/a6f+vWrVW6dGn99NNPjv1auXv37nr22Wf1559/Zjmt3I42bdrogw8+0KeffuqemipJ8+fPzzJ8s2bN1KpVKz333HP68ccfdccddyg8PDzHNNq3b6+JEydq9uzZuv/++93bFyxYoBMnTqh9+/aZ9gkICFDz5s1Vt25dzZkzR1u2bHEPcGQIDw9X165ddebMGV177bXavn17jl/8dujQQY899piefPJJXXTRRapbt657+yeffKLExMRcPYrq3Bk1YWFhtvc7X/fu3TV+/HiVKVMm24v5rNJu06aNSpcurc8++0xbt27NcoAjN/22QoUKCg0N1bZt2zy2f/zxx5nitSwr04350qVL9eeff+riiy+2VYZz5aUOcrtv165dNWHCBL3++ut66qmnsg1n5xdV52rZsqXCwsI0e/Zs9e3b17197969Wr16tfr06WM7rpx07txZgYGB+u9//+vYI9O6d++u+fPnKy0tTc2bN89THG3atNHzzz+vn376yeMxVdmdQ3r37q0qVapo5MiRWrt2rV588UWmqAMACkReP6Nr166tJ554QgsWLNCWLVvc23NzjeDEdXpWceb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JHDNx4kS9/fbb2rlzZ+q13/72t+rVq5eGDRuW1fKi+2UyZj788EP16tV++di7d29Jf/mLJOCTWP/2bFwz5T+uYfIf1xT5jTV+/mPN3TOxnsmxXO5oXmjuv/9+17dvX3ffffe5l156yV155ZVuwIAB7o033nDOOXfttde6mTNnpuJfe+01d9BBB7mrrrrKvfTSS+6+++5zffv2dQ8++GB3VQE51tkxU11d7fr06eO+//3vu3feeSf188EHH3RXFZBjnR0z+1u4cKE74YQTclRadLfOjpcdO3a4YcOGua997Wvu17/+tVu7dq0bNWqUu/jii7urCsixzo6ZZcuWuT59+ri77rrL/f73v3fr1q1zJ510kjvllFO6qwrIsR07drhNmza5TZs2OUnu1ltvdZs2bXJ/+MMfnHOsf3EgrpnyH9cw+Y9rivzGGj//sebOf6yB440bHF30/e9/3x1xxBEukUi4z33uc27t2rWpYxdccIE744wz2sWvWbPGnXjiiS6RSLgjjzzS3X333TkuMbpbZ8bMGWec4SQd8HPBBRfkvuDoNp2dZz6Ji5Gep7Pj5eWXX3aTJk1y/fv3d8OGDXNz5sxxH374YY5Lje7U2TFzxx13uGOPPdb179/fHXrooa6iosK99dZbOS41ussTTzwRujZh/YuOcM2U/7iGyX9cU+Q31vj5jzV3fmMNHG+BczzbBAAAAAAAAAAA8gt7cAAAAAAAAAAAgLzDDQ4AAAAAAAAAAJB3uMEBAAAAAAAAAADyDjc4AAAAAAAAAABA3uEGBwAAAAAAAAAAyDvc4AAAAAAAAAAAAHmHGxwAAAAAAAAAACDvcIMDAAAAAAAAAADkHW5wAAAAAAAAAACAvMMNDgBAXiorK9OVV17Z3cUAAAAA0M24NgCAnosbHAAAZOD0009XEAQH/FRUVJjef+GFF+raa6+NLD0AAAAA3YNrAwDoPn26uwAAgJ6jpaVFiUSiu4vRZc45bd68WTfffPMBFxmf+tSnvO/fu3evHnnkEa1YsSKS9AAAAIB8w7XBR7g2AICu4QkOAOihnHO66aabdNRRR6l///464YQT9OCDD6aOl5WVafbs2brmmmuUTCY1ZMgQXX/99Z1OY9asWZozZ44+/elP6+yzz5Yk7dixQxUVFRowYIAOPfRQ3Xbbbe0eK//JT36igQMHqrm5uV1+X/3qV/UP//APHdanublZs2fP1qBBg9SvXz+ddtppeu6551LHH3zwQR1//PHq37+/Bg4cqEmTJmnXrl3m45/0u9/9Tjt27NDpp5+uIUOGtPuxXHQ8/fTT6tWrl8aPHx9JegAAAEBXcG3AtQEA5CtucABAD/Wv//qvWrZsme6++279+te/1lVXXaW///u/19q1a1MxP/7xjzVgwABt2LBBN910k2644QatXr2602n06dNHTz/9tH7wgx9IkubMmaOnn35aK1as0OrVq/XUU0/phRdeSL3n61//utra2lJ/xSRJ77//vlauXKlvfvObHdbnmmuu0UMPPaQf//jHeuGFFzRy5Eidc845qq+v1zvvvKMZM2boH//xH/Xyyy9rzZo1+spXviLnnCR5j+9v48aN6tOnj8aOHZtBy0srVqzQl770JfXq1SuS9AAAAICu4NqAawMAyFsOANDj7Ny50/Xr18+tX7++3esXXXSRmzFjhnPOuTPOOMOddtpp7Y6ffPLJbt68eZ1K47Of/Wy7401NTa5v377uv/7rv1KvffDBB+6ggw5yV1xxReq1f/7nf3ZTp05N/f/22293Rx11lNu7d28q7X3xO3fudH379nXLly9Pxbe0tLihQ4e6m266yW3cuNFJcm+88UaH7eE7vr+5c+e6IAjcgAED2v1cfPHFpvcfffTRbsWKFZ1K7xe/+IU7+uij3ciRI929995rygcAAADw4dqgPa4NACC/sAcHAPRAL730kv785z+nHgvfp6WlRSeeeGLq//v/1dChhx6qd999t1NpnHTSSe2Ov/baa9qzZ49OOeWU1GslJSUaPXp0u7hLLrlEJ598sv74xz/qsMMO07Jly3ThhRcqCIID6vP73/9ee/bs0cSJE1Ov9e3bV6eccopefvllzZkzR2eddZaOP/54nXPOOZo8ebK+9rWvqbS0VJJ0wgknhB7f38aNG/X1r39dixcvbvd6uvhPevnll/XWW29p0qRJ5vRaW1s1Z84cPfHEEyouLtbnPvc5feUrX1EymfTmBwAAAITh2oBrAwDIZ9zgAIAeaO/evZKkRx55RIcddli7Y0VFRal/9+3bt92xIAhS77WmMWDAgHbH3MePdu9/MeL2e+T7xBNP1AknnKCf/OQnOuecc/SrX/1Kv/jFLzqsT1iaQRCod+/eWr16tdavX69Vq1bpzjvv1Pz587VhwwaNGDHCe3x/mzZt0g033KCRI0d2WJ4XX3xR5513np5++mkNGTJE77//viZNmqRnn31WK1as0Nlnn63+/fub03v22Wd13HHHpdr5b//2b/XYY49pxowZHcYDAAAAVlwbcG0AAPmMPTgAoAc69thjVVRUpDfffFMjR45s9zN8+PCspvHXf/3X6tu3r5599tnUa01NTfrd7353QOzFF1+sZcuW6Yc//KEmTZqUNt2RI0cqkUho3bp1qdf27Nmj559/Xsccc4ykjy5wJk6cqMrKSm3atEmJREK1tbWpeN/xfV577TV98MEH7f4SbX9jxozR3/3d3+nxxx+XJFVWVmrevHlKJBJ6+OGHde6553YqvbfffrvdheKwYcP0xz/+MW08AAAAYMW1AdcGAJDPeIIDAHqggw8+WHPnztVVV12lvXv36rTTTlNTU5PWr1+vT33qU7rggguylsbBBx+sCy64QP/yL/+iZDKpQYMGaeHCherVq9cBf2VVUVGhuXPn6t5779VPfvKTtGUZMGCA/vmf/zmV5uGHH66bbrpJH374oS666CJt2LBB//d//6fJkydr0KBB2rBhg957773UBY7v+Cdt3LhRkjR48GBt27at3bFBgwalNgc87rjj9Nvf/lavvvqqNm7cqDvuuEPvvvuunnvuOf33f/93p9Lb/y/YpAP/Ig0AAADIBNcGXBsAQD7jBgcA9FCLFi3SoEGDtGTJEr322ms65JBD9LnPfU7f+c53sp7GrbfeqksvvVTTpk1TcXGxrrnmGm3dulX9+vVrF1dcXKyvfvWreuSRR/TlL385NM3vfve72rt3r2bOnKkdO3bopJNO0mOPPabS0lIVFxfrySef1O23366mpiYdccQRuuWWWzR16tRUPmHHP+mFF16QJB199NHtXu/bt6927NiRegR/1KhRWrlypb7zne9o8eLFCoJAv/jFLzR+/HgNGjSoU+kddthh7f4q66233tL48eND2wMAAACw4tqAawMAyFeB6+jWLwAAObRr1y4ddthhuuWWW3TRRRe1O3b22WfrmGOO0R133NFNpcvMBx98oFGjRmn8+PFauXKlJOncc8/VaaedpmuuuaZTabW2tuqYY47RmjVrUhsJPvPMMxo4cGA2ig4AAAB0G64NwnFtAADt8QQHACDnNm3apN/85jc65ZRT1NjYqBtuuEGSdN5556Vi6uvrtWrVKj3++OOqqqrqrqJm7JBDDpH00V+P7XPaaadltPlfnz59dMstt+jMM8/U3r17dc0113ABAwAAgILAtUHncG0AAO3xBAcAIOc2bdqkiy++WK+88ooSiYTGjRunW2+9Vccff3wq5sgjj1RDQ4MWLFiguXPndmNpM7Nnzx6NGTNGr7zySncXBQAAAIgtrg0AAF3BExwAgJw78cQTUxvopfPGG2/kpjBZ8pvf/EajR4/u7mIAAAAAsca1AQCgK3iCAwAAAAAAAAAA5J1e3V0AAAAAAAAAAACAzuIGBwAAAAAAAAAAyDvc4AAAAAAAAAAAAHmHGxwAAAAAAAAAACDvcIMDAAAAAAAAAADkHW5wAAAAAAAAAACAvMMNDgAAAAAAAAAAkHe4wQEAAAAAAAAAAPIONzgAAAAAAAAAAEDe4QYHAAAAAAAAAADIO9zgAAAAAAAAAAAAeef/A4atnWS7HGpJAAAAAElFTkSuQmCC",
|
|
"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",
|
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"zv_lost = scifi_lost[\"velo_track_z\"]\n",
|
|
"\n",
|
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"\n",
|
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"\n",
|
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"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()"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
|
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"execution_count": 32,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
|
"data": {
|
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"text/html": [
|
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"<pre>[101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 101,\n",
|
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" 0]\n",
|
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"------------------\n",
|
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"type: 11 * float32</pre>"
|
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],
|
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"text/plain": [
|
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"<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
|
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]
|
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},
|
|
"execution_count": 32,
|
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"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": {
|
|
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rbZWY0dIEgLoQ07ZKfVRf87a/F3httR7njGzFm9hEREREREREREREbod7YhMREREREREREREROR2/iU1ERERERERERETkdhrR+rd3W3uP7dbBm9hEpMjcPlW6yWrPmNlPTmWPa1mncg6Ffc1aNEwLViwfVaS87zIAzMYi5SdU9nN7MeQl1WPlblDeK9knVn1P6o9CUhTL1faEvi1EfU/s7CPWnQMA1Iavtr+1uX2XX/jXAquOZU58iPJ59JPr1Rvdr1w8H8r7TgPq+3Wb23v7+b8rj+eFIcrjT/lBeX9zQP21UdsTHVDfrx1vKRd7J9WoHmv75FGK5eb2yvZ+S/l4DWp7BiarHgpItX4/TLVrgC17a3LfPSIicqS2+DljzTlaf59kC/cltmIvZ6W9pe3bo1vpHJb12/72lvXb0v247e2jEkv3kVZUpXQO+15/JcrjtmyfdcZ6RNrAm9hEREREREREREREbod7YhMREREREREREREROR2/iU1ERERERERERETkdprQ+ntYu+Y3sXkTm4iIiIiIiIiIiMjtaGc7Ed7EJiKrqSZjXL9VvVGycsITnx+Uk8fN/0ElEyHMJ11UbTNZpY1KMrrtPygnwgMAXYhY3UYt6d/sEOVxqiVPBADde8rnH4kdqm3CR+cplr94v/K8jFynfqwxk1SSAaokHAQAfKJcfOldH8Vy/Q71xI5q82wuseKLF5THud1D/TVTS26pi1Kef1mgUz1WzSLlBJJ5aSrJGwHM/0E9UaW1FmG21W0adignUDWzzNR1sj7ZjqgkXdWpTzMREZHbcdckdY5ImqjTmX6WsDyBpFKCRPsSRdrzOtgzP/YnvTSdC0VVSudRmm/Teo54/ZUojdvSWNBd31tEpI57YreCpUuXok+fPvD19UVMTAzy8/Od3SUiIiIiIrIDY3wiIiJyfY0O+ud6eBPbThs3bsTMmTPx3HPPoaioCLfddhvGjBmDsrIyZ3eNiIiIiIhswBifiIiIyLXwJradFi9ejClTpuCPf/wjoqKisGTJEvTs2RPvvPOOs7tGREREREQ2YIxPRERE7qHJQf9cD/fEtkNDQwMOHTqEOXPmGJUnJiaioEB5L9X6+nrU1/+61+ulS5cAADU1yvsCE7mXOvWnflZe46Ky9us91fdEhii3aar5Sb1No8p77Gfl4p9qzFy0Vc5vro214zQ3FLW5NDf+H9WeUJkXs3OpMn61ufzlPMrFNTXK+0urngPq86w2xwBse53VqMxZjZklqzpOtXUJoN7cAa3UBJXXU2UtmaUyFLN/cabSxrYffcrXGf4cJVfUsi5F1N445IoY41PbMv255nrrxkyMfxXr+m3PMZXamka7zp1He15Xa8Ziz1zYXs/+ddsW8+MO7y1yZ64T57Xe50bHHtN+OnH+bLutc+fO4YYbbsDevXsRHx9vKF+wYAHWrl2LEydOmLTJyspCdrZlSQyIiIiIyP2dOXMGN954o7O7QRZijE9ERESWclacV1dXhz59+uD8+fMOOX5oaChKS0vh6+vrkOPbgt/EbgW6a9LjiohJWYu5c+di1qxZhsfV1dWIiIhAWVkZ9Hq9Q/vZ3tTU1KBnz54oLy9HcHCws7vjVjh39uH82Y5zZzvOne04d/bh/Nnu0qVL6NWrF0JCQpzdFbIBY3zbaP2aoeXxc+zaHDug7fFreeyAtsfv7DjP19cXpaWlaGhocMjxvb29XeoGNsCb2Hbp0qULPDw8TH7rUVlZie7duyu28fHxgY+Pj0m5Xq/X3Bu+tQQHB3PubMS5sw/nz3acO9tx7mzHubMP5892HTowDY07YYzfOrR+zdDy+Dl2bY4d0Pb4tTx2QNvjd2ac5+vr63I3mh2JEbUdvL29ERMTg127dhmV79q1y+hPD4mIiIiIyD0wxiciIiJyPfwmtp1mzZqFtLQ0xMbGIi4uDitWrEBZWRkyMjKc3TUiIiIiIrIBY3wiIiIi18Kb2HaaOHEiLl68iBdeeAEVFRWIjo7Gp59+ioiICIva+/j4IDMzU/HPD8k8zp3tOHf24fzZjnNnO86d7Th39uH82Y5z574Y49tOy2MHtD1+jl2bYwe0PX4tjx3Q9vi1PHZn0YmIOLsTRERERERERERERERKuCc2EREREREREREREbks3sQmIiIiIiIiIiIiIpfFm9hERERERERERERE5LJ4E5uIiIiIiIiIiIiIXBZvYjvYSy+9hPj4ePj7+6Njx44WtRERZGVlITw8HH5+fhg5ciT+97//GdWpr6/HU089hS5duiAgIAD33HMPvvnmGweMwHmqqqqQlpYGvV4PvV6PtLQ0VFdXm22j0+kU/73yyiuGOiNHjjR5ftKkSQ4eTduzZf4eeughk7kZMWKEUR2uPVONjY2YPXs2Bg8ejICAAISHh+PBBx/EuXPnjOq1x7W3dOlS9OnTB76+voiJiUF+fr7Z+rt370ZMTAx8fX1x4403YtmyZSZ1Nm/ejIEDB8LHxwcDBw7E1q1bHdV9p7Nm/rZs2YK7774bXbt2RXBwMOLi4rBz506jOmvWrFG8BtbV1Tl6KG3OmrnLy8tTnJfjx48b1dPK2rNm7pR+Luh0OgwaNMhQRyvrbs+ePfjDH/6A8PBw6HQ6fPjhh9dtw2te+6XlGF/rMbqWY2ytxchajnO1HqNqOc7UapzIOM9NCDnU888/L4sXL5ZZs2aJXq+3qM3ChQslKChINm/eLMXFxTJx4kQJCwuTmpoaQ52MjAy54YYbZNeuXXL48GEZNWqU3HLLLdLU1OSgkbS9pKQkiY6OloKCAikoKJDo6GhJTk4226aiosLoX05Ojuh0Ojlz5oyhTkJCgkydOtWoXnV1taOH0+Zsmb/09HRJSkoympuLFy8a1eHaM1VdXS133XWXbNy4UY4fPy6FhYUyfPhwiYmJMarX3tbehg0bxMvLS1auXCklJSUyY8YMCQgIkLNnzyrW/+qrr8Tf319mzJghJSUlsnLlSvHy8pIPPvjAUKegoEA8PDxkwYIFcuzYMVmwYIF4enrKvn372mpYbcba+ZsxY4YsWrRIPv/8czl58qTMnTtXvLy85PDhw4Y6q1evluDgYJNrYXtj7dzl5uYKADlx4oTRvFx93dLK2rN27qqrq43mrLy8XEJCQiQzM9NQRyvr7tNPP5XnnntONm/eLABk69atZuvzmte+aTnG13qMruUYW0sxspbjXK3HqFqOM7UcJzLOcw+8id1GVq9ebVGA29zcLKGhobJw4UJDWV1dnej1elm2bJmI/HKh8PLykg0bNhjqfPvtt9KhQwfZsWNHq/fdGUpKSgSA0Zu7sLBQAMjx48ctPk5KSorccccdRmUJCQkyY8aM1uqqS7J1/tLT0yUlJUX1ea49y9fe559/LgCMfuC3t7U3bNgwycjIMCobMGCAzJkzR7H+X/7yFxkwYIBR2WOPPSYjRowwPJ4wYYIkJSUZ1Rk9erRMmjSplXrtOqydPyUDBw6U7Oxsw2NLf9a4O2vnruXDRVVVleoxtbL27F13W7duFZ1OJ19//bWhTCvr7mqWfLjhNU8btBbjaz1G13KMrbUYWctxrtZjVC3HmYwTf8E4z3VxOxEXU1paivPnzyMxMdFQ5uPjg4SEBBQUFAAADh06hMbGRqM64eHhiI6ONtRxd4WFhdDr9Rg+fLihbMSIEdDr9RaP8bvvvsO2bdswZcoUk+fee+89dOnSBYMGDcIzzzyD2traVuu7K7Bn/vLy8tCtWzf0798fU6dORWVlpeE5rj3Lx3jp0iXodDqTPzFuL2uvoaEBhw4dMloLAJCYmKg6T4WFhSb1R48ejYMHD6KxsdFsnfayvlrYMn/Xam5uRm1tLUJCQozKf/zxR0RERKBHjx5ITk5GUVFRq/XbFdgzd0OGDEFYWBjuvPNO5ObmGj2nhbXXGutu1apVuOuuuxAREWFU3t7XnS14zaOrtZcYX+sxupZjbC3FyFqOc7Ueo2o5zmScaJ328p53N57O7gAZO3/+PACge/fuRuXdu3fH2bNnDXW8vb3RqVMnkzot7d3d+fPn0a1bN5Pybt26WTzGtWvXIigoCOPHjzcqT01NRZ8+fRAaGoovv/wSc+fOxdGjR7Fr165W6bsrsHX+xowZg/vuuw8REREoLS3F/Pnzcccdd+DQoUPw8fHh2rNwjHV1dZgzZw4mT56M4OBgQ3l7WnsXLlzAlStXFK9VavN0/vx5xfpNTU24cOECwsLCVOu0l/XVwpb5u9arr76Kn376CRMmTDCUDRgwAGvWrMHgwYNRU1OD119/HbfeeiuOHj2Kfv36teoYnMWWuQsLC8OKFSsQExOD+vp6vPvuu7jzzjuRl5eH22+/HYD6+mxPa8/edVdRUYHt27dj3bp1RuVaWHe24DWPrtZeYnytx+hajrG1FCNrOc7Veoyq5TiTcaJ12st73t3wJrYNsrKykJ2dbbbOgQMHEBsba/M5dDqd0WMRMSm7liV1nM3SuQNM5wCwbow5OTlITU2Fr6+vUfnUqVMN/x8dHY1+/fohNjYWhw8fxtChQy06trM4ev4mTpxo+P/o6GjExsYiIiIC27ZtM/mgYc1xXUFbrb3GxkZMmjQJzc3NWLp0qdFz7rz21Fh7rVKqf225Ldc/d2XrWNevX4+srCx89NFHRh8oR4wYYZQo6tZbb8XQoUPx5ptv4o033mi9jrsAa+YuMjISkZGRhsdxcXEoLy/H3//+d8OHC2uP6c5sHeeaNWvQsWNHjBs3zqhcS+vOWrzmuRctx/haj9G1HGMzRlan5ThX6zGqluNMxomWa0/veXfBm9g2mDZt2nWzJffu3dumY4eGhgL45bc6YWFhhvLKykrDb3BCQ0PR0NCAqqoqo9/WV1ZWIj4+3qbzthVL5+6LL77Ad999Z/Lc999/b/KbLCX5+fk4ceIENm7ceN26Q4cOhZeXF06dOuXyNxLbav5ahIWFISIiAqdOnQLAtXe9uWtsbMSECRNQWlqKzz77zOgbJkrcae1dq0uXLvDw8DD5LfLV16prhYaGKtb39PRE586dzdaxZt26A1vmr8XGjRsxZcoUvP/++7jrrrvM1u3QoQN++9vfGt7D7YE9c3e1ESNG4J///KfhsRbWnj1zJyLIyclBWloavL29zdZtj+vOFrzmuR8tx/haj9G1HGMzRjal5ThX6zGqluNMxonWaS/vebfj+G23ScT6pC+LFi0ylNXX1ysmfdm4caOhzrlz51wq8Ye9WhKH7N+/31C2b98+ixOHpKenm2S9VlNcXCwAZPfu3Tb319XYO38tLly4ID4+PrJ27VoR4dozN3cNDQ0ybtw4GTRokFRWVlp0Lndfe8OGDZPHH3/cqCwqKspswpuoqCijsoyMDJPkF2PGjDGqk5SU1C6TX1g7fyIi69atE19f3+smGmnR3NwssbGx8vDDD9vTVZdjy9xd695775VRo0YZHmtl7dk6dy1Ji4qLi697jva67q4GCxP+8JrX/mktxtd6jK7lGFtrMbKW41ytx6hajjMZJ/6CcZ7r4k1sBzt79qwUFRVJdna2BAYGSlFRkRQVFUltba2hTmRkpGzZssXweOHChaLX62XLli1SXFws999/v4SFhUlNTY2hTkZGhvTo0UP+/e9/y+HDh+WOO+6QW265RZqamtp0fI6UlJQkN998sxQWFkphYaEMHjxYkpOTjepcO3ciIpcuXRJ/f3955513TI55+vRpyc7OlgMHDkhpaals27ZNBgwYIEOGDGlXcydi/fzV1tbKn/70JykoKJDS0lLJzc2VuLg4ueGGG7j2rjN3jY2Ncs8990iPHj3kyJEjUlFRYfhXX18vIu1z7W3YsEG8vLxk1apVUlJSIjNnzpSAgABDNuo5c+ZIWlqaof5XX30l/v7+8vTTT0tJSYmsWrVKvLy85IMPPjDU2bt3r3h4eMjChQvl2LFjsnDhQvH09JR9+/a1+fgczdr5W7dunXh6esrbb79ttMaqq6sNdbKysmTHjh1y5swZKSoqkocfflg8PT2NPnC2B9bO3WuvvSZbt26VkydPypdffilz5swRALJ582ZDHa2sPWvnrsUDDzwgw4cPVzymVtZdbW2tIY4DIIsXL5aioiI5e/asiPCapzVajvG1HqNrOcbWUoys5ThX6zGqluNMLceJjPPcA29iO1h6eroAMPmXm5trqANAVq9ebXjc3NwsmZmZEhoaKj4+PnL77beb/Ebr8uXLMm3aNAkJCRE/Pz9JTk6WsrKyNhpV27h48aKkpqZKUFCQBAUFSWpqqlRVVRnVuXbuRESWL18ufn5+Rj80W5SVlcntt98uISEh4u3tLTfddJNMnz5dLl686MCROIe18/fzzz9LYmKidO3aVby8vKRXr16Snp5usq649n5x9dyVlpYqvs+vfq+317X39ttvS0REhHh7e8vQoUONvjGTnp4uCQkJRvXz8vJkyJAh4u3tLb1791b8IPv+++9LZGSkeHl5yYABA4wCwPbGmvlLSEhQXGPp6emGOjNnzpRevXqJt7e3dO3aVRITE6WgoKANR9R2rJm7RYsWyU033SS+vr7SqVMn+d3vfifbtm0zOaZW1p6179vq6mrx8/OTFStWKB5PK+uu5VtGau9BXvO0RcsxvtZjdC3H2FqLkbUc52o9RtVynKnVOJFxnnvQifz/zuNERERERERERERERC6mg7M7QERERERERERERESkhjexiYiIiIiIiIiIiMhl8SY2EREREREREREREbks3sQmIiIiIiIiIiIiIpfFm9hERERERERERERE5LJ4E5uIiIiIiIiIiIiIXBZvYhMRERERERERERGRy+JNbCIiIiIiIiIiIiJyWbyJTUTUxkaOHImZM2c6uxuakJWVBZ1OB51OhyVLljilD7179zb0obq62il9ICIiImoLjHPbDuNcItIa3sQmIiKneeihhzBu3DiHnmPQoEGoqKjAo48+6tDzqDlw4AA2b97slHMTERERkXMwziUial28iU1EpGGNjY3O7kKraGhoUH3O09MToaGh8Pf3b8Me/apr164ICQlxyrmJiIiItIpxruMxziWitsSb2ERETtDU1IRp06ahY8eO6Ny5M+bNmwcRUa2flZWF3/zmN1i+fDl69uwJf39/3HfffUZ/tnfgwAHcfffd6NKlC/R6PRISEnD48GGj4+h0OixbtgwpKSkICAjAX//6V1y5cgVTpkxBnz594Ofnh8jISLz++utG7Vq+SbJgwQJ0794dHTt2RHZ2NpqamvDnP/8ZISEh6NGjB3Jycozaffvtt5g4cSI6deqEzp07IyUlBV9//bVhTGvXrsVHH31k+DPEvLy867a7uj8vv/wywsPD0b9/f6vmX6fTYfny5UhOToa/vz+ioqJQWFiI06dPY+TIkQgICEBcXBzOnDlj8hrk5OSgV69eCAwMxOOPP44rV67gb3/7G0JDQ9GtWze89NJLVvWFiIiIqD1hnMs4l4jIEXgTm4jICdauXQtPT0/s378fb7zxBl577TX84x//MNvm9OnT2LRpEz7++GPs2LEDR44cwZNPPml4vra2Funp6cjPz8e+ffvQr18/jB07FrW1tUbHyczMREpKCoqLi/HII4+gubkZPXr0wKZNm1BSUoLnn38ezz77LDZt2mTU7rPPPsO5c+ewZ88eLF68GFlZWUhOTkanTp2wf/9+ZGRkICMjA+Xl5QCAn3/+GaNGjUJgYCD27NmD//73vwgMDERSUhIaGhrwzDPPYMKECUhKSkJFRQUqKioQHx9/3XYt/vOf/+DYsWPYtWsXPvnkE6tfgxdffBEPPvggjhw5ggEDBmDy5Ml47LHHMHfuXBw8eBAAMG3aNKM2Z86cwfbt27Fjxw6sX78eOTk5+P3vf49vvvkGu3fvxqJFizBv3jzs27fP6v4QERERtQeMcxnnEhE5hBARUZtKSEiQqKgoaW5uNpTNnj1boqKiVNtkZmaKh4eHlJeXG8q2b98uHTp0kIqKCsU2TU1NEhQUJB9//LGhDIDMnDnzun184okn5N577zU8Tk9Pl4iICLly5YqhLDIyUm677Taj8wUEBMj69etFRGTVqlUSGRlpNM76+nrx8/OTnTt3Go6bkpJidG5L23Xv3l3q6+vNjiMzM1NuueUWk3IAMm/ePMPjwsJCASCrVq0ylK1fv158fX2NjuXv7y81NTWGstGjR0vv3r1N5uXll182Ol9ubq4AkKqqKrP9JSIiInJnjHMZ5xIROQq/iU1E5AQjRoyATqczPI6Li8OpU6dw5coV1Ta9evVCjx49jNo0NzfjxIkTAIDKykpkZGSgf//+0Ov10Ov1+PHHH1FWVmZ0nNjYWJNjL1u2DLGxsejatSsCAwOxcuVKk3aDBg1Chw6//tjo3r07Bg8ebHjs4eGBzp07o7KyEgBw6NAhnD59GkFBQQgMDERgYCBCQkJQV1dn9OeL17K03eDBg+Ht7a16nOu5+eabjcbScsyry+rq6lBTU2Mo6927N4KCgozqDBw40GReWuaAiIiISGsY5zLOJSJyBE9nd4CIiGzT8uGg5b8PPfQQvv/+eyxZsgQRERHw8fFBXFycSTKYgIAAo8ebNm3C008/jVdffRVxcXEICgrCK6+8gv379xvV8/LyMjm/UllzczMAoLm5GTExMXjvvfdM+t61a1fVcVna7tpxWOvqvrfMoVJZy3iufb6ljrk5ICIiIiLrMc5lnEtEdC3exCYicoJr95Jr2dvPw8NDtU1ZWRnOnTuH8PBwAEBhYSE6dOhgSPaSn5+PpUuXYuzYsQCA8vJyXLhw4bp9yc/PR3x8PJ544glDmblvkFhq6NCh2LhxI7p164bg4GDFOt7e3ibfyrGkHRERERG5Jsa5v2CcS0TUuridCBGRE5SXl2PWrFk4ceIE1q9fjzfffBMzZsww28bX1xfp6ek4evQo8vPzMX36dEyYMAGhoaEAgL59++Ldd9/FsWPHsH//fqSmpsLPz++6fenbty8OHjyInTt34uTJk5g/fz4OHDhg9xhTU1PRpUsXpKSkID8/H6Wlpdi9ezdmzJiBb775BsAvf7b4xRdf4MSJE7hw4QIaGxstakdERERErolxLuNcIiJH4E1sIiInePDBB3H58mUMGzYMTz75JJ566ik8+uijZtv07dsX48ePx9ixY5GYmIjo6GgsXbrU8HxOTg6qqqowZMgQpKWlYfr06ejWrdt1+5KRkYHx48dj4sSJGD58OC5evGj0bRVb+fv7Y8+ePejVqxfGjx+PqKgoPPLII7h8+bLhmydTp05FZGSkYZ/CvXv3WtSOiIiIiFwT41zGuUREjqATEXF2J4iIyLysrCx8+OGHOHLkiLO74lZcZd7y8vIwatQoVFVVoWPHjk7tCxEREZErcZV4zd24yrwxziWitsJvYhMRUbtWXFyMwMBAo2/ztKVBgwZhzJgxTjk3EREREbVfjHOJSEuY2JGIiNqt6dOn44EHHgBgPlO8I3366adobGwEAP6ZKBERERG1Csa5RKQ13E6EiIiIiIiIiIiIiFwWtxMhIiIiIiIiIiIiIpfFm9hERERERERERERE5LJ4E5uIiIiIiIiIiIiIXBZvYhMRERERERERERGRy+JNbCIiIiIiIiIiIiJyWbyJTUREREREREREREQuizexiYiIiIiIiIiIiMhl8SY2EREREREREREREbms/wNoUWTCCpnujwAAAABJRU5ErkJggg==",
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"text/plain": [
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"<Figure size 1800x600 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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}
|
|
],
|
|
"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": [
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"<Figure size 1800x600 with 3 Axes>"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
|
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"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
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"\n",
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"a0=ax[0].hist2d(bs_found, vtx_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
|
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"ax[0].set_ylim(0,110)\n",
|
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"ax[0].set_xlim(-1,1)\n",
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"ax[0].set_xlabel(\"b\")\n",
|
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"ax[0].set_ylabel(\"endvtx id\")\n",
|
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"ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
|
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"ax[0].set_yticks(np.arange(0,110,1),minor=True)\n",
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"\n",
|
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"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",
|
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"ax[1].set_yticks(np.arange(0,110,1), minor=True)\n",
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"\n",
|
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"\"\"\"\n",
|
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"vtx_id: 101 - Bremsstrahlung\n",
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"B:\n",
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"wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n",
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"\"\"\"\n",
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"fig.colorbar(a0[3], ax=ax, orientation='vertical')\n",
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"plt.show()"
|
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<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": []
|
|
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