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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import uproot\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",
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"import seaborn as sns\n",
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"from scipy.optimize import curve_fit\n",
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"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
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"file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
"#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
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"\n",
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"#look at particles only from Signal\n",
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"allcolumns = file.arrays()\n",
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"tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)]\n",
"lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] \n",
"\n",
"#ak.num(tracked, axis=0)\n",
"\n"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
"#lost\n",
"l_eph = lost[\"brem_photons_pe\"]\n",
"ak.nan_to_num(l_eph)\n",
"l_pT = lost[\"pt\"]\n",
"l_sci_x = lost[\"scifi_hit_pos_x\"]\n",
"ak.nan_to_num(l_sci_x)\n",
"\n",
"#found\n",
"f_eph = tracked[\"brem_photons_pe\"]\n",
"ak.nan_to_num(f_eph)\n",
"f_pT = tracked[\"pt\"]\n",
"f_sci_x = tracked[\"scifi_hit_pos_x\"]\n",
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"ak.nan_to_num(f_sci_x)\n",
"\n",
"l_sci_x, l_pT = ak.broadcast_arrays(l_sci_x, l_pT)\n",
"f_sci_x, f_pT = ak.broadcast_arrays(f_sci_x, f_pT)\n",
"\n",
"l_sci_x = ak.to_numpy(ak.flatten(l_sci_x))\n",
"l_pT = ak.to_numpy(ak.flatten(l_pT))\n",
"f_sci_x = ak.to_numpy(ak.flatten(f_sci_x))\n",
"f_pT = ak.to_numpy(ak.flatten(f_pT))"
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]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
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"<Figure size 2000x600 with 4 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
"\n",
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"a0=ax0.hist2d(l_sci_x, l_pT, bins=200, cmap=plt.cm.jet, cmin=0)\n",
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"ax0.set_xlabel(\"scifi x\")\n",
"ax0.set_ylabel(r\"$p_T$\")\n",
"ax0.set_title(\"lost electron positions in the scifi in regard to their transverse momentum\")\n",
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"plt.colorbar(a0[3],ax=ax0)\n",
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"\n",
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"a1=ax1.hist2d(f_sci_x, f_pT, bins=200, cmap=plt.cm.jet, cmin=0)\n",
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"ax1.set_xlabel(\"scifi x\")\n",
"ax1.set_ylabel(r\"$p_T$\")\n",
"ax1.set_title(\"found electron positions in the scifi in regard to their transverse momentum\")\n",
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"plt.colorbar(a1[3],ax=ax1)\n",
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"\n",
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"\"\"\"\n",
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"B:\n",
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"we can see that the lost electrons cover a wider spread in the x direction of the scifi tracker,\n",
"while most of those have low pT \n",
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"\n",
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"\"\"\"\n",
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"plt.show()"
]
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},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[4.09e+04], [8.66e+03], [8.07e+04], ..., [5.63e+03], [6.29e+03], [2.26e+03]]\n",
"[4.62e+04, 9.36e+03, 1.34e+05, 5.63e+04, ..., 2.01e+04, 6.94e+03, 7.83e+03]\n",
"8657.132\n",
"9355.866625028413\n"
]
}
],
"source": [
"energy_found = tracked[\"energy\"]\n",
"energy_found = energy_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
"#ak.nan_to_num(energy_found)\n",
"\n",
"e_ph_found = tracked[\"brem_photons_pe\"]\n",
"e_ph_found = e_ph_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
"#ak.nan_to_num(e_ph_found, nan=[0])\n",
"e_ph_found = ak.sum(e_ph_found, axis=-1, keepdims=True)\n",
"print(e_ph_found)\n",
"print(energy_found)\n",
"\n",
"energy_lost = lost[\"energy\"]\n",
"energy_lost = energy_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
"#ak.nan_to_num(energy_lost)\n",
"\n",
"e_ph_lost = lost[\"brem_photons_pe\"]\n",
"e_ph_lost = e_ph_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
"#ak.nan_to_num(e_ph_lost)\n",
"e_ph_lost = ak.sum(e_ph_lost, axis=-1,keepdims=True)\n",
"\n",
"#e_ph_found, energy_found = ak.broadcast_arrays(e_ph_found, energy_found)\n",
"#e_ph_lost, energy_lost = ak.broadcast_arrays(e_ph_lost, energy_lost)\n",
"\n",
"e_ph_found = ak.to_numpy(ak.flatten(e_ph_found))\n",
"energy_found = ak.to_numpy(energy_found)\n",
"\n",
"e_ph_lost = ak.to_numpy(ak.flatten(e_ph_lost))\n",
"energy_lost = ak.to_numpy(energy_lost)\n",
"\n",
"print(e_ph_found[1])\n",
"print(energy_found[1])"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [],
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"source": [
"q_e_found = e_ph_found/energy_found\n",
"q_e_lost = e_ph_lost/energy_lost"
]
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},
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{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
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"plt.hist(q_e_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
"plt.hist(q_e_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
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"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
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"plt.ylabel(\"counts (normed)\")\n",
"plt.title(r'$E_{ph}/E_0$')\n",
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"plt.legend()\n",
"\n",
"\"\"\"\n",
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"B:\n",
"we can clearly see that lost electrons are responsible for higher energy photons\n",
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"\"\"\"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
"outputs": [
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{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 2000x600 with 4 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
"\n",
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"a0 = ax0.hist2d(e_ph_found/(1e3), energy_found/(1e3), density=False, bins=200, cmap=plt.cm.jet, range=[[0,200],[0,200]], cmin=1)\n",
"ax0.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n",
"ax0.set_ylabel(r\"$E_e$ [GeV]\")\n",
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"ax0.set_title(\"found electron energy against photon energy\")\n",
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"plt.colorbar(a0[3],ax=ax0)\n",
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"\n",
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"a1 = ax1.hist2d(e_ph_lost/(1e3), energy_lost/(1e3), density=False, bins=200, cmap=plt.cm.jet, range=[[0,200],[0,200]], cmin=1)\n",
"ax1.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n",
"ax1.set_ylabel(r\"$E_e$ [GeV]\")\n",
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"ax1.set_title(\"lost electron energy against photon energy\")\n",
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"plt.colorbar(a1[3],ax=ax1)\n",
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"\n",
"\"\"\"\n",
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"B:\n",
"concentrated at the E_ph/E_0~1 line especially at lower energies.\n",
"lost E_ph to E_0: fewer entries at lower q_e\n",
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"\"\"\"\n",
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"plt.show()"
]
},
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{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [],
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"source": [
"brem_vtx_x_found = tracked[\"brem_vtx_x\"]\n",
"brem_vtx_x_found = brem_vtx_x_found[tracked[\"brem_vtx_x_length\"]!=0]\n",
"brem_vtx_x_found = ak.to_numpy(ak.flatten(brem_vtx_x_found))\n",
"\n",
"brem_vtx_z_found = tracked[\"brem_vtx_z\"]\n",
"brem_vtx_z_found = brem_vtx_z_found[tracked[\"brem_vtx_z_length\"]!=0]\n",
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"#print(ak.to_numpy(brem_vtx_z_found))\n",
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"brem_vtx_z_found = ak.to_numpy(ak.flatten(brem_vtx_z_found))\n",
"\n",
"brem_vtx_x_lost = lost[\"brem_vtx_x\"]\n",
"brem_vtx_x_lost = brem_vtx_x_lost[lost[\"brem_vtx_x_length\"]!=0]\n",
"brem_vtx_x_lost = ak.to_numpy(ak.flatten(brem_vtx_x_lost))\n",
"\n",
"brem_vtx_z_lost = lost[\"brem_vtx_z\"]\n",
"brem_vtx_z_lost = brem_vtx_z_lost[lost[\"brem_vtx_z_length\"]!=0]\n",
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"brem_vtx_z_lost = ak.to_numpy(ak.flatten(brem_vtx_z_lost))\n",
"\n",
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"#vtx_x_fit= ak.to_numpy(vtx_x_found)\n",
"#vtx_z_fit = ak.to_numpy(vtx_z_found)"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"\n"
]
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},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 2000x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
"\n",
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"a0 = ax0.hist2d(brem_vtx_z_found, brem_vtx_x_found, density=False, bins=300, cmap=plt.cm.jet, cmin=1)\n",
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"ax0.set_xlabel(\"z [mm]\")\n",
"ax0.set_ylabel(\"x [mm]\")\n",
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"ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
"\n",
"plt.colorbar(a0[3],ax=ax0)\n",
"\n",
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"a1 = ax1.hist2d(brem_vtx_z_lost, brem_vtx_x_lost, density=False, bins=300, cmap=plt.cm.jet, cmin=1)\n",
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"ax1.set_xlabel(\"z [mm]\")\n",
"ax1.set_ylabel(\"x [mm]\")\n",
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"ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
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"#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
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"\n",
"plt.colorbar(a1[3], ax=ax1)\n",
"\n",
"\"\"\"\n",
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"z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
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"B:\n",
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"vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
"found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
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"\"\"\"\n",
"plt.show()"
]
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},
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{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [],
"source": [
"#plot singular tracks by fitting brem vertices\n",
"vtx_z_found = tracked[\"brem_vtx_z\"]\n",
"vtx_z_found = vtx_z_found[tracked[\"brem_vtx_z_length\"]>3]\n",
"\n",
"vtx_x_found = tracked[\"brem_vtx_x\"]\n",
"vtx_x_found = vtx_x_found[tracked[\"brem_vtx_x_length\"]>3]\n",
"\n",
"vtx_z_lost = lost[\"brem_vtx_z\"]\n",
"vtx_z_lost = vtx_z_lost[lost[\"brem_vtx_z_length\"]>3]\n",
"\n",
"vtx_x_lost = lost[\"brem_vtx_x\"]\n",
"vtx_x_lost = vtx_x_lost[lost[\"brem_vtx_x_length\"]>3]\n",
"\n",
"def cubic_fit(x, a, b, c, d):\n",
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" return (a + b*x + c*x**2 + d*x**3)\n",
"\n",
"def quint_fit(x, a, b, c, d, e, f):\n",
" return (a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5)\n"
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]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/scipy/optimize/_minpack_py.py:1010: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n"
]
},
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{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 2000x600 with 2 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
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"n_end=100\n",
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"\n",
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"for i in range(0,n_end):\n",
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" popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]))\n",
" z_coord = np.linspace(vtx_z_found[i,0],12000,1000)\n",
" fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
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" ax0.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n",
" ax0.errorbar(ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]),fmt=\".\",ms=2)\n",
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"\n",
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"#ax0.legend()\n",
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"ax0.set_xlabel(\"z [mm]\")\n",
"ax0.set_ylabel(\"x [mm]\")\n",
"ax0.set_title(\"found tracks of brem vertices from few signals\")\n",
"ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
"ax0.grid()\n",
"\n",
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"for i in range(0,n_end):\n",
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" popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]))\n",
" z_coord = np.linspace(vtx_z_lost[i,0],12000,1000)\n",
" fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
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" ax1.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n",
" ax1.errorbar(ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]),fmt=\".\",ms=2)\n",
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"\n",
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"#ax1.legend()\n",
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"ax1.set_xlabel(\"z [mm]\")\n",
"ax1.set_ylabel(\"x [mm]\")\n",
"ax1.set_title(\"lost tracks of brem vertices from few signals\")\n",
"ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
"ax1.grid()\n",
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"\n",
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"\"\"\"\n",
"we can see that of the lost brem vertices, many trajectory fits seem illogical and not plausible\n",
"\"\"\"\n",
"\n",
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"plt.show()"
]
},
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{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [],
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"source": [
"endvtx_x_found = tracked[\"all_endvtx_x\"]\n",
"endvtx_x_found = endvtx_x_found[tracked[\"all_endvtx_x_length\"]!=0]\n",
"endvtx_x_found = ak.to_numpy(ak.flatten(endvtx_x_found))\n",
"\n",
"endvtx_z_found = tracked[\"all_endvtx_z\"]\n",
"endvtx_z_found = endvtx_z_found[tracked[\"all_endvtx_z_length\"]!=0]\n",
"#print(ak.to_numpy(brem_vtx_z_found))\n",
"endvtx_z_found = ak.to_numpy(ak.flatten(endvtx_z_found))\n",
"\n",
"endvtx_x_lost = lost[\"all_endvtx_x\"]\n",
"endvtx_x_lost = endvtx_x_lost[lost[\"all_endvtx_x_length\"]!=0]\n",
"endvtx_x_lost = ak.to_numpy(ak.flatten(endvtx_x_lost))\n",
"\n",
"endvtx_z_lost = lost[\"all_endvtx_z\"]\n",
"endvtx_z_lost = endvtx_z_lost[lost[\"all_endvtx_z_length\"]!=0]\n",
"endvtx_z_lost = ak.to_numpy(ak.flatten(endvtx_z_lost))"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2000x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
"\n",
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"a0 = ax0.hist2d(endvtx_z_found, endvtx_x_found, density=False, bins=500, cmap=plt.cm.jet, cmin=1)\n",
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"ax0.set_xlabel(\"z [mm]\")\n",
"ax0.set_ylabel(\"x [mm]\")\n",
"ax0.set_title(r\"$e^\\pm$ found end vertices\")\n",
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"ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
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"\n",
"plt.colorbar(a0[3],ax=ax0)\n",
"\n",
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"a1 = ax1.hist2d(endvtx_z_lost, endvtx_x_lost, density=False, bins=500, cmap=plt.cm.jet, cmin=1)\n",
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"ax1.set_xlabel(\"z [mm]\")\n",
"ax1.set_ylabel(\"x [mm]\")\n",
"ax1.set_title(r\"$e^\\pm$ lost end vertices\")\n",
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"ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
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"\n",
"plt.colorbar(a1[3], ax=ax1)\n",
"\n",
"\"\"\"\n",
"z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
"B:\n",
"vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
"found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
"\"\"\"\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
"# try to plot trajectories using all tracker hits (Velo, UT, SciFi)\n",
"\n",
"velo_x_found = tracked[\"velo_hit_pos_x\"]\n",
"velo_z_found = tracked[\"velo_hit_pos_z\"]\n",
"ut_x_found = tracked[\"ut_hit_pos_x\"]\n",
"ut_z_found = tracked[\"ut_hit_pos_z\"]\n",
"scifi_x_found = tracked[\"scifi_hit_pos_x\"]\n",
"scifi_z_found = tracked[\"scifi_hit_pos_z\"]\n",
"\n",
"tracker_x_found = ak.concatenate([velo_x_found,ut_x_found,scifi_x_found], axis=1)\n",
"tracker_z_found = ak.concatenate([velo_z_found,ut_z_found,scifi_z_found], axis=1)\n",
"\n",
"velo_x_lost = lost[\"velo_hit_pos_x\"]\n",
"velo_z_lost = lost[\"velo_hit_pos_z\"]\n",
"ut_x_lost = lost[\"ut_hit_pos_x\"]\n",
"ut_z_lost = lost[\"ut_hit_pos_z\"]\n",
"scifi_x_lost = lost[\"scifi_hit_pos_x\"]\n",
"scifi_z_lost = lost[\"scifi_hit_pos_z\"]\n",
"\n",
"tracker_x_lost = ak.concatenate([velo_x_lost,ut_x_lost,scifi_x_lost], axis=1)\n",
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"tracker_z_lost = ak.concatenate([velo_z_lost,ut_z_lost,scifi_z_lost], axis=1)\n",
"\n",
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"\n",
"tracker_x_found = tracker_x_found[tracked[\"energy\"]<10*1e3]\n",
"tracker_z_found = tracker_z_found[tracked[\"energy\"]<10*1e3]\n",
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"\n",
"\n",
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"tracker_x_lost = tracker_x_lost[lost[\"energy\"]<10*1e3]\n",
"tracker_z_lost = tracker_z_lost[lost[\"energy\"]<10*1e3]"
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]
},
{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
"outputs": [],
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"source": []
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},
{
"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 2000x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
"\n",
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"nstart=0\n",
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"nend=130\n",
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"\n",
"for i in range(nstart,nend):\n",
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" popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]))\n",
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" z_coord = np.linspace(tracker_z_found[i,0],14000,1000)\n",
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" fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
2023-09-19 14:22:04 +02:00
" ax0.plot(z_coord, fit, \"-\", lw=0.5)\n",
2023-09-19 10:52:00 +02:00
" ax0.errorbar(ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]),fmt=\".\",ms=3)\n",
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"\n",
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"ax0.legend([r\"$E<10$GeV\"])\n",
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"ax0.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
"ax0.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
2023-09-19 11:06:50 +02:00
"ax0.set_xticks(np.arange(0,14000,1000) , minor=True)\n",
2023-09-19 10:52:00 +02:00
"ax0.set_yticks(np.arange(-4000,4000,500), minor=True)\n",
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"ax0.set_xlabel(\"z [mm]\")\n",
"ax0.set_ylabel(\"x [mm]\")\n",
"ax0.set_title(\"found tracks from detector hits from few signals\")\n",
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"ax0.set(xlim=(0,14000), ylim=(-4000,4000))\n",
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"ax0.grid()\n",
"\n",
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"for i in range(nstart,nend):\n",
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" popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]))\n",
2023-09-19 11:06:50 +02:00
" z_coord = np.linspace(tracker_z_lost[i,0],14000,1000)\n",
2023-09-19 09:58:54 +02:00
" fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
2023-09-19 14:22:04 +02:00
" ax1.plot(z_coord, fit, \"-\", lw=0.5)\n",
2023-09-19 10:52:00 +02:00
" ax1.errorbar(ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]),fmt=\".\",ms=3)\n",
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"\n",
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"ax1.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
"ax1.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
2023-09-19 11:06:50 +02:00
"ax1.set_xticks(np.arange(0,14000,1000) , minor=True)\n",
2023-09-19 10:52:00 +02:00
"ax1.set_yticks(np.arange(-4000,4000,500), minor=True)\n",
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"ax1.set_xlabel(\"z [mm]\")\n",
"ax1.set_ylabel(\"x [mm]\")\n",
"ax1.set_title(\"lost tracks from detector hits from few signals\")\n",
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"ax1.set(xlim=(0,14000), ylim=(-4000,4000))\n",
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"ax1.grid()\n",
"\n",
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"\n",
"\"\"\"\n",
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"electrons and photons will be stopped by the ECAL which serves to measure the particles energy\n",
"\n",
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"the trajectories between the velo and tt should be linear, which cannot be plotted accurately using a single fit.\n",
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"lost tracks diverge more severely.\n",
"\n",
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"most higher energy particles maintain a trajectory closer to the beamdirection ie a larger pseudorapidity,\n",
"and show less bending in their trajectory, especially upstream.\n",
"found: higher energy: very compact trajectory, less bending wrt lower energy particles \n",
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"\"\"\"\n",
"\n",
"\n",
"\n",
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"plt.show()"
]
2023-09-18 16:44:11 +02:00
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
2023-09-19 10:52:00 +02:00
{
"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": []
},
2023-09-19 09:58:54 +02:00
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'all_endvtx_types_length': 11,\n",
" 'all_endvtx_types': [101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 101.0,\n",
" 0.0],\n",
" 'all_endvtx_x_length': 11,\n",
" 'all_endvtx_x': [19.496400833129883,\n",
" 24.957000732421875,\n",
" 32.490699768066406,\n",
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" 'all_endvtx_y_length': 11,\n",
" 'all_endvtx_y': [-20.370500564575195,\n",
" -26.043100357055664,\n",
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" -593.3890991210938],\n",
" 'all_endvtx_z_length': 11,\n",
" 'all_endvtx_z': [399.7018127441406,\n",
" 500.7049865722656,\n",
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" 11237.0966796875],\n",
" 'brem_photons_pe_length': 10,\n",
" 'brem_photons_pe': [2469.182861328125,\n",
" 169.8916473388672,\n",
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" 'brem_photons_px_length': 10,\n",
" 'brem_photons_px': [133.6999969482422,\n",
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" 'brem_photons_py_length': 10,\n",
" 'brem_photons_py': [-137.9199981689453,\n",
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" -5.800000190734863,\n",
" -36.66999816894531],\n",
" 'brem_photons_pz_length': 10,\n",
" 'brem_photons_pz': [2461.699951171875,\n",
" 169.36000061035156,\n",
" 223.00999450683594,\n",
" 387.1199951171875,\n",
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" 'brem_vtx_x_length': 10,\n",
" 'brem_vtx_x': [19.496400833129883,\n",
" 24.957000732421875,\n",
" 32.490699768066406,\n",
" 34.14419937133789,\n",
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" -3782.947998046875,\n",
" -3786.80810546875],\n",
" 'brem_vtx_y_length': 10,\n",
" 'brem_vtx_y': [-20.370500564575195,\n",
" -26.043100357055664,\n",
" -33.85060119628906,\n",
" -35.53160095214844,\n",
" -36.069400787353516,\n",
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" -533.6621704101562,\n",
" -598.8317260742188,\n",
" -599.1124877929688],\n",
" 'brem_vtx_z_length': 10,\n",
" 'brem_vtx_z': [399.7018127441406,\n",
" 500.7049865722656,\n",
" 637.5723266601562,\n",
" 667.4852905273438,\n",
" 677.0195922851562,\n",
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" 8576.556640625,\n",
" 9281.2119140625,\n",
" 11183.1640625,\n",
" 11188.4541015625],\n",
" 'endvtx_type': 0,\n",
" 'endvtx_x': nan,\n",
" 'endvtx_y': nan,\n",
" 'endvtx_z': nan,\n",
" 'energy': 9355.866625028413,\n",
" 'eta': 3.237728027535365,\n",
" 'event_count': 2,\n",
" 'fromB': True,\n",
" 'fromD': False,\n",
" 'fromDecay': True,\n",
" 'fromHadInt': False,\n",
" 'fromPV': False,\n",
" 'fromPairProd': False,\n",
" 'fromSignal': True,\n",
" 'fromStrange': False,\n",
" 'isElectron': True,\n",
" 'isKaon': False,\n",
" 'isMuon': False,\n",
" 'isPion': False,\n",
" 'isProton': False,\n",
" 'lost': False,\n",
" 'lost_in_track_fit': False,\n",
" 'match_fraction': 1.0,\n",
" 'mcp_idx': 5488,\n",
" 'mother_id': 511,\n",
" 'mother_key': 5479,\n",
" 'originvtx_type': 2,\n",
" 'originvtx_x': -0.0663,\n",
" 'originvtx_y': -0.0023,\n",
" 'originvtx_z': 40.3966,\n",
" 'p': 9355.866611073503,\n",
" 'phi': -0.8090232566094933,\n",
" 'pid': -11,\n",
" 'pt': 733.3612464536151,\n",
" 'px': 506.17,\n",
" 'py': -530.67,\n",
" 'pz': 9327.08,\n",
" 'scifi_hit_pos_x_length': 13,\n",
" 'scifi_hit_pos_x': [-1402.2215576171875,\n",
" -1448.5460205078125,\n",
" -1495.479736328125,\n",
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" 'scifi_hit_pos_y_length': 13,\n",
" 'scifi_hit_pos_y': [-470.2232666015625,\n",
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" -530.0570068359375,\n",
" -532.8589477539062,\n",
" -535.61572265625,\n",
" -538.2548217773438],\n",
" 'scifi_hit_pos_z_length': 13,\n",
" 'scifi_hit_pos_z': [7824.40576171875,\n",
" 7894.1943359375,\n",
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},
2023-09-14 12:07:57 +02:00
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