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Projektpraktikum/B_updown.ipynb

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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": {
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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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a2/3xxx8ZDnNis9nk4+OjWbNmKSEhQV27dtWpU6fS3X9UVJTi4+O1YcOGTPN8oylTprg0H42r10VWrp/M7NixQ9LV4Uvsdrtjua+vr2655ZZMhxeTpEKFCqlXr1769ttvdfLkSb355ps6cuSI2rRpo4iIiHTzddttt2nSpEm67777dNddd6lXr17avHmzSpQooeeeey7T/datW1e+vr6O1zvvvCNJio+P1+rVq3X//ferQIECTmV17733Kj4+Xlu3bnVKq0+fPvrjjz+0ZMkSSVJiYqJmzJihu+66S5UqVco0L+70yCOPOL3v2rWrfHx8tHbtWklS/fr1NXXqVL3++uvaunWrEhISsrSfG//urZRbYmKixowZo+rVq8vPz08+Pj7y8/PToUOH9NNPP6Xal7vvDykyK6sU18+TJEm1atVSfHy8Tp8+LSlr97zrZXX7zPKV1XPdvHlz/fLLLzpy5Iji4+O1ceNGtWnTRs2aNdOqVaskSdHR0fL393cM45hiwYIFLg/TJbl+Dm7mekvhqetIknr37q1Lly5p7ty5jmWff/65/P391b17d5fS6Nq1q5588kk9++yzev311/XSSy+pZcuWlvJxo4EDB2r58uWaN2+ebr/99jRj9u3bp/DwcHXq1El//PGH3nvvPZ06dUpLly7VI4884nRNpuepp57SwYMHNXDgQP3+++86fvy4/vWvfznK0cvr6tcjVu/bNpst3X3euM5K7M1eC568lgAAyA9oOAEAAG4TExOjPXv26H//+5/8/f2dvvD19fXVpUuXVKhQoWzJW4kSJZzenz17VsaYVMslOeaR+PPPP52WFy5c2Om9n5+fChQooICAgFTL4+PjM81TysTsHTt2VL169VSvXj21bt1ac+bMkbe3tz755JM0twsODtYnn3yS6SvlS7xOnTqpSJEiae4/ICBAn332maKiovTCCy84vlR3J1evC09dPzt37pSvr2+qceUl6cSJEypdurSl9OLi4nTu3DnFxsbKGKNChQrJx8fH5e0LFSqk9u3ba9++fbp06ZKKFi2qwMDANL+4mjVrlnbs2JHqvPz5559KTEzU5MmTU5XTvffeK0mpGoQ6d+4su92uzz//XNLViYpPnTp1U5PCpxx3enMbJCYmytfXN9Xy8PDwVOkUKVLE8Tc3d+5c9ejRQ//5z3/UqFEjFS5cWI899phiYmIs5e/Gv28r5TZkyBANHz5c9913n5YuXapt27Zpx44dql27ti5dupRqX+6+P6TIrKxS3Pg37u/vL0mOvGblnne9rG6fWb6yeq5btGgh6WrjyMaNG5WQkKB77rlHLVq0cMzjER0drSZNmigwMNCx3fbt23Xs2DFLDSeunoObud5SeOo6kqRbb71Vd9xxh+MekJSUpBkzZqhTp06p9puR3r17KyEhQT4+Pho0aJClPNzo9ddf10cffaSPP/5Ybdq0STfO19dXdrtdSUlJio2NVWxsrC5cuGBpX71799abb76p6dOnq1SpUipTpox+/PFHDRs2TJJUsmTJdLe98b6dIq3rQJL++usvSc7n00psWu+tXguevJYAAMgPXK/hAQAAZOL48eOSrk5MfuMvfFNcP2HuP+nGX3KGhobKy8srzclgT5w4IUkqWrSoR/O0e/du1axZU97e3k7LfX195e3tneaXs9LVLx4ff/zxDNNevny5li1bps6dO2v27NlpfrG/e/du1a5dWz4+Pvrkk08cExhv3LgxzQnrs8rV6+Lw4cMuxVm1c+dOFS1aNNWXRdu2bdMvv/yi4cOHZ5rG8ePHNW/ePM2ZM0c7duxQyZIl9dBDD+mTTz5RvXr1LOfJGCPp6nXp7e2te+65RytXrtTJkyedvnytXr26JKWaLDk0NNTxS/8BAwakuY/y5cs7vQ8MDFS3bt30ySef6OTJk/rss88UHBycZoOSq8LCwiRJv//+u+P/1x/jyZMn0yyfmJgYpy8pExMT9eeffzq+ZC9atKgmTZqkSZMm6dixY1qyZIleeOEFnT59WitWrHA5f2n93btabjNmzNBjjz2mMWPGOK3/448//tEG4MzKylU3e8/z1D0zq+e6VKlSqly5sqKjo1WuXDnVq1dPhQoVUvPmzdW/f39t27ZNW7du1ejRo522mz9/vipXrqwaNWq4nEdXz8HNXG//lF69eql///766aef9Msvv+jkyZPq1auXy9tfvHhRUVFRqly5sk6dOqXHH39cixcvzlJepk6dquHDh2vUqFHq3bt3hrHVqlXTL7/8oi1btmjWrFl68803NXToUDVp0kQPPfSQOnfunKqBKy3PP/+8Bg8erEOHDik4OFhly5ZVv379FBQUpLp162a47fX37RQ1a9bU7NmzlZiY6PQ5+8MPP0iS03VmJRYAAGQ/Gk4AAIDbpPy60WazZenL5H9SUFCQGjRooAULFmj8+PGOXyQnJydrxowZji/lPCU2Nla//PKLmjdvnmrd4sWLFR8fr7vvvjvL6Y8bN04dOnRIt9EkZf+tWrWSdLUxZuHChapfv746duyo7du3u/QllCtcvS48df3s3LlTsbGxOnfunOML76SkJD3//PMqV65cukPUnD9/XlOnTtXcuXO1efNmhYaG6sEHH9Rbb72lyMhIx7AuVp09e1bLli1TnTp1HI05L774or755hv961//0ldffZVmL43rFShQQM2aNdOePXtUq1Yt+fn5ubTvPn366KOPPtK4ceP09ddfq2fPnipQoECWjkOS7rnnHtlsNs2dOzfVEDsrVqxQXFyco2fA9WbOnOn0JeWXX36pxMRENW3aNFVsmTJlNHDgQK1evVqbNm1yLL+x54IrrJSbzWZz7CPF8uXL9fvvv+uWW25xeZ83y0pZZcTKPS+tsv0n7pnpnev0tGjRQl9++aVKly6tdu3aSZIqV66sMmXKaMSIEUpISEh1/c2fP19du3a1lK+snoOs/p16Urdu3TRkyBBNnTpVv/zyi0qWLOn4HEiR0d/Wv/71Lx07dkzbt2/Xf//7X3Xu3FkTJ07UM888YykfK1asUN++fdW7d2+NHDnS5e0aNWqkRo0aadKkSVq9erVmzZqll19+WU8//bQiIyP10EMP6bHHHnPqZXQjf39/RyPFsWPHNHfuXPXt2zfDbdK6b0vS/fffr08++UTz58/XQw895Fg+bdo0RUREqEGDBlmKBQAA2Y+GEwAA4DYVK1ZUs2bN9Morr+jChQtq0KCB41fna9euVY8ePSx/2edJY8eOVcuWLdWsWTMNGzZMfn5++uCDD7R//37Nnj07w7HIb9bu3btljFFQUJBjjPuzZ89q8+bNmjhxomrVquUYPiQrli5dqsDAwHSHkErZ//VfBoaHh2vx4sW688471bFjR61fvz7DL5Jc5ep1YfX6sdlsioyM1Lp169Ld95EjR/Tnn3+qTJky6tKli4YOHar4+Hi999572rVrl9atW5ful5m7du3SCy+8oI4dO2rRokVq27Ztpg0aN+revbvKlCmjevXqqWjRojp06JDeeecdnTp1SlOnTnXENWnSRO+//76eeuop3X777XriiSd06623On7hP3/+fElSSEiIY5t3331Xd955p+666y49+eSTKleunM6fP6/Dhw9r6dKlWrNmTar81KtXT7Vq1dKkSZNkjEl3mC5Xyla6em4HDhyocePG6dy5c7r33nsVGBioHTt26M0331S9evXSbJhasGCBfHx81LJlSx04cEDDhw9X7dq11bVrV8XGxqpZs2bq3r27qlatquDgYMf8Pw888IAjjZo1azrKoUePHvL19VWVKlUUHBycYZ5dLbf27dtr6tSpqlq1qmrVqqVdu3Zp3LhxKlWqVIbpu1tGZWWVq/e89MrW3fdMV891epo3b64PPvhAf/zxhyZNmuS0/PPPP1doaKjTPW7v3r363//+Z2mYLunmzkFW/k49qVChQrr//vs1depUnTt3TsOGDUvVCJze+Z87d65mzJihzz//XLfeeqtuvfVWDRw4UM8//7yaNGmi+vXru5SHI0eOqEuXLqpQoYJ69eqVap6X2267LVWj5Y28vb3VqlUrtWrVSh999JGWL1+uWbNmafDgwWrQoEGavSb379+v+fPnq169evL399f333+vN998U5UqVdJrr73miHP1vi1Jbdu2VcuWLfXkk08qLi5Ot9xyi2bPnq0VK1ZoxowZTj1KrcQCAIAcIFumpAcAAHlWbGysefHFF03lypVNQECACQ0NNbVr1zZPPfWUOXv2rEf3vXbtWiPJzJs3z7Fs5MiRRpI5c+ZMmtt899135p577jFBQUEmMDDQNGzY0CxdutQpJr00evToYYKCglKlGRkZaW699dYM8zp+/HgjyekVFBRkbrvtNvPGG2+YixcvunrYWZKy/927d6daN2/ePGOz2UyXLl1McnKyW/bn6nXhatz58+eNJPPwww9nuN8vv/zSSDKbN282UVFRJiQkxAQHB5tOnTqZH3/8MdM8X7hw4WYO24wdO9bUqVPH2O124+3tbYoVK2buv/9+s3379jTj9+7da3r16mXKly9v/P39TUBAgLnlllvMY489ZlavXp0q/siRI6Z3796mZMmSxtfX1xQrVsw0btzYvP766+nm6d133zWSTPXq1dNc72rZpkhOTjYffvihqVevnilQoIDx8/MzlSpVMs8//7w5f/68U2zK39KuXbtMhw4dTMGCBU1wcLDp1q2bOXXqlDHGmPj4ePOvf/3L1KpVy4SEhJjAwEBTpUoVM3LkyFR/Fy+++KKJiIgwXl5eRpJZu3at037S+7t3pdzOnj1r+vTpY4oXL24KFChg7rzzTvPdd9+ZyMhIExkZmeqY3Hl/cLWsMtr/559/biSZI0eOOC135Z5nTPpl6+r2ruTLyrlOy9mzZ42Xl5cJCgoyV65ccSyfOXOmkWQeeOABp/hXXnnFlC1bNtN0bzyGrJ6DFK5cb566jtKycuVKx+fOwYMH04y58fy/9957JjAw0PTo0cM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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
}