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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import uproot\t\n", "import numpy as np\n", "import sys\n", "import os\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits import mplot3d\n", "import itertools\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\n", "from mpl_toolkits.axes_grid1 import ImageGrid\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10522" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "\n", "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n", "allcolumns = file.arrays()\n", "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n", "\n", "ak.num(found, axis=0) + ak.num(lost, axis=0)\n", "#ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff all = 0.8606728758791105 +/- 0.003375885792719708\n" ] } ], "source": [ "def t_eff(found, lost, axis = 0):\n", " sel = ak.num(found, axis=axis)\n", " des = ak.num(lost, axis=axis)\n", " return sel/(sel + des)\n", "\n", "def eff_err(found, lost):\n", " n_f = ak.num(found, axis=0)\n", " n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n", " return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n", "\n", "\n", "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#try excluding all photons that originate from a vtx @ z>9500mm\n", "#ignore all brem vertices @ z>9500mm \n", "\n", "#found\n", "\n", "brem_e_f = found[\"brem_photons_pe\"]\n", "brem_z_f = found[\"brem_vtx_z\"]\n", "e_f = found[\"energy\"]\n", "length_f = found[\"brem_vtx_z_length\"]\n", "\n", "brem_f = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(found,axis=0)):\n", " brem_f.begin_record()\n", " #[:,\"energy\"] energy\n", " brem_f.field(\"energy\").append(e_f[itr])\n", " #[:,\"photon_length\"] number of vertices\n", " brem_f.field(\"photon_length\").integer(length_f[itr])\n", " #[:,\"brem_photons_pe\",:] photon energy \n", " brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n", " #[:,\"brem_vtx_z\",:] brem vtx z\n", " brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n", " brem_f.end_record()\n", "\n", "brem_f = ak.Array(brem_f)\n", "\n", "#lost\n", "\n", "brem_e_l = lost[\"brem_photons_pe\"]\n", "brem_z_l = lost[\"brem_vtx_z\"]\n", "e_l = lost[\"energy\"]\n", "length_l = lost[\"brem_vtx_z_length\"]\n", "\n", "brem_l = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(lost,axis=0)):\n", " brem_l.begin_record()\n", " #[:,\"energy\"] energy\n", " brem_l.field(\"energy\").append(e_l[itr])\n", " #[:,\"photon_length\"] number of vertices\n", " brem_l.field(\"photon_length\").integer(length_l[itr])\n", " #[:,\"brem_photons_pe\",:] photon energy \n", " brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n", " #[:,\"brem_vtx_z\",:] brem vtx z\n", " brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n", " brem_l.end_record()\n", "\n", "brem_l = ak.Array(brem_l)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split in Upstream and Downstream Events and analyse separately" ] }, { "cell_type": "code", "execution_count": 5, "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(brem_f, axis=0)):\n", " upstream_found.begin_record()\n", " upstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n", " \n", " downstream_found.begin_record()\n", " downstream_found.field(\"energy\").real(brem_f[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(brem_f[itr, \"photon_length\"]):\n", " if (brem_f[itr, \"brem_vtx_z\", jentry]>5000):\n", " if brem_f[itr, \"brem_vtx_z\", jentry]<9500:\n", " downstream_found.real(brem_f[itr,\"brem_photons_pe\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_found.real(brem_f[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(brem_f[itr, \"photon_length\"]):\n", " if brem_f[itr, \"brem_vtx_z\", jentry]>5000:\n", " if brem_f[itr,\"brem_vtx_z\",jentry]<9500:\n", " downstream_found.real(brem_f[itr,\"brem_vtx_z\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_found.real(brem_f[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(brem_l, axis=0)):\n", " upstream_lost.begin_record()\n", " upstream_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n", " \n", " downstream_lost.begin_record()\n", " downstream_lost.field(\"energy\").real(brem_l[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(brem_l[itr, \"photon_length\"]):\n", " if (brem_l[itr, \"brem_vtx_z\", jentry]>5000):\n", " if brem_l[itr, \"brem_vtx_z\", jentry]<9500:\n", " downstream_lost.real(brem_l[itr,\"brem_photons_pe\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_lost.real(brem_l[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(brem_l[itr, \"photon_length\"]):\n", " if brem_l[itr, \"brem_vtx_z\", jentry]>5000:\n", " if brem_l[itr,\"brem_vtx_z\",jentry]<9500:\n", " downstream_lost.real(brem_l[itr,\"brem_vtx_z\",jentry])\n", " else:\n", " continue\n", " else:\n", " upstream_lost.real(brem_l[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": 15, "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": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upstream_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "cutoff = 0 MeV, sample size: 880\n", "eff = 0.9125 +/- 0.0095\n", "\n", "cutoff = 100 MeV, sample size: 880\n", "eff = 0.9125 +/- 0.0095\n", "\n", "cutoff = 200 MeV, sample size: 1279\n", "eff = 0.921 +/- 0.0075\n", "\n", "cutoff = 300 MeV, sample size: 1587\n", "eff = 0.9175 +/- 0.0069\n", "\n", "cutoff = 400 MeV, sample size: 1827\n", "eff = 0.9152 +/- 0.0065\n", "\n", "cutoff = 500 MeV, sample size: 2072\n", "eff = 0.9146 +/- 0.0061\n", "\n", "cutoff = 600 MeV, sample size: 2266\n", "eff = 0.9144 +/- 0.0059\n", "\n", "cutoff = 700 MeV, sample size: 2445\n", "eff = 0.9121 +/- 0.0057\n", "\n", "cutoff = 800 MeV, sample size: 2615\n", "eff = 0.9117 +/- 0.0055\n", "\n", "cutoff = 900 MeV, sample size: 2765\n", "eff = 0.9114 +/- 0.0054\n", "\n", "cutoff = 1000 MeV, sample size: 2910\n", "eff = 0.9096 +/- 0.0053\n", "\n", "upstream: cutoff energy = 200MeV, sample size: 1279\n", "eff = 0.921 +/- 0.008\n" ] } ], "source": [ "up_efficiencies = ak.ArrayBuilder()\n", "\n", "\n", "\n", "for cutoff_energy in range(0,1050,100):\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", "\n", "\n", "\n", "\tprint(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n", "\tprint(\"eff = \",np.round(t_eff(up_nobrem_f,up_nobrem_l),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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "cutoff = 0 MeV, sample size: 3971\n", "eff = 0.893 +/- 0.0049\n", "\n", "cutoff = 100 MeV, sample size: 3971\n", "eff = 0.893 +/- 0.0049\n", "\n", "cutoff = 200 MeV, sample size: 4634\n", "eff = 0.8869 +/- 0.0047\n", "\n", "cutoff = 300 MeV, sample size: 5096\n", "eff = 0.8872 +/- 0.0044\n", "\n", "cutoff = 400 MeV, sample size: 5414\n", "eff = 0.886 +/- 0.0043\n", "\n", "cutoff = 500 MeV, sample size: 5654\n", "eff = 0.885 +/- 0.0042\n", "\n", "cutoff = 600 MeV, sample size: 5881\n", "eff = 0.8845 +/- 0.0042\n", "\n", "cutoff = 700 MeV, sample size: 6079\n", "eff = 0.8842 +/- 0.0041\n", "\n", "cutoff = 800 MeV, sample size: 6251\n", "eff = 0.8831 +/- 0.0041\n", "\n", "cutoff = 900 MeV, sample size: 6426\n", "eff = 0.8828 +/- 0.004\n", "\n", "cutoff = 1000 MeV, sample size: 6561\n", "eff = 0.882 +/- 0.004\n", "\n", "downstream: cutoff energy = 200MeV, sample size: 4634\n", "eff = 0.8869 +/- 0.005\n" ] } ], "source": [ "down_efficiencies = ak.ArrayBuilder()\n", "for cutoff_energy in range(0,1050,100):\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", "\n", "\n", "\n", "\tprint(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n", "\tprint(\"eff = \",np.round(t_eff(down_nobrem_f,down_nobrem_l),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": 8, "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": 9, "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": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x600 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#in abhängigkeit von der energie der elektronen\n", "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n", "\n", "\n", "ax[0].hist(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "ax[0].hist(up_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "ax[0].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n", "ax[0].set_yticks(np.arange(0,11,1), minor=True)\n", "ax[0].set_xlabel(r\"$E_\\gamma/E_0$\")\n", "ax[0].set_ylabel(\"counts (normed)\")\n", "ax[0].set_title(\"Upstream\")\n", "ax[0].legend()\n", "ax[0].grid()\n", "\n", "ax[1].hist(down_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "ax[1].hist(down_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "ax[1].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n", "ax[1].set_yticks(np.arange(0,11,1), minor=True)\n", "ax[1].set_xlabel(r\"$E_\\gamma/E_0$\")\n", "ax[1].set_ylabel(\"counts (normed)\")\n", "ax[1].set_title(\"Downstream\")\n", "ax[1].legend()\n", "ax[1].grid()\n", "\n", "\"\"\"\n", "electrons that lose most of their energy downstream are almost always lost\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": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABj8AAAJOCAYAAADoCxXRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADR+klEQVR4nOzde5yN5f7/8fcyZ2NmzJAZI8eShKEoxN7OpxyKjaImVLJTSWiXTkM7h5yyN0lbB3Ku7bCL9oSIhHJItvSldgoxKGMcwpiZ6/dHv1nbMqd7mXtZa9Z6PR+PeZR7fdZ1uq/7Xte9rnXdt8MYYwQAAAAAAAAAAOAnSnm7AAAAAAAAAAAAAHZi8gMAAAAAAAAAAPgVJj8AAAAAAAAAAIBfYfIDAAAAAAAAAAD4FSY/AAAAAAAAAACAX2HyAwAAAAAAAAAA+BUmPwAAAAAAAAAAgF9h8gMAAAAAAAAAAPgVJj8AAAAAAAAAAIBfYfIDAADAx+3atUsHDhzwdjEAAAAAACgxmPwAAADwcf/4xz+0du1abxcDAAAAAIASg8kPAAAAH7RhwwYNHDhQx44dc27bs2ePevTooaNHj3qxZAAAAAAA+D4mPwAAgN/76KOP5HA4XP6io6PVsGFDvffee1cl73fffddle3p6ujp16qTQ0FBNnz49z/saNmyo+Ph4JSUl6ZNPPtH06dPVrl07dezYUddcc41Hy1ySfPrpp3n2be7fli1b8n3Prl279OCDD+q6665TRESEIiIiVLNmTQ0aNEjbtm1zuwzdu3dXRESETp48WWDMvffeq5CQELcnrkaNGiWHw6Fffvkl39fr1q2rli1bupWmOzZt2qRRo0YVWrdAUNR+cFcgtGt2drYqVKigV199tcAYu9u1pPDn/f/ll1+qQ4cOioqKUpkyZdSqVSt9/vnneeLcOXefOXNGQ4cOVWJiosLDw9WgQQMtWrQo3/zdiQUAAP4v2NsFAAAA8LQdO3ZIkv71r3+pQoUKMsbowIEDSklJUZ8+fXTjjTcqKSnJo3k3bNjQuW3Xrl3q3r27zp49q7Vr16p58+Z53hcZGamXXnpJOTk5GjdunEqVKqX58+frnnvu8Ug5S7qxY8eqVatWLtvq1q2bJ+6NN97QY489plq1aumJJ55QnTp15HA49O2332rhwoW69dZb9f333+u6666znPeDDz6o5cuXa8GCBRo8eHCe1zMyMrRs2TJ16dJF8fHx7lfOizZt2qTRo0erf//+Klu2rLeL4zcCoV03bNig48ePq0ePHt4uis/x1/2/detW/fGPf9Rtt92muXPnyhijCRMmqE2bNlq3bp2aNm2a5z1Wzt09evTQ1q1bNX78eN1www1asGCB+vTpo5ycHPXt2/eKYwEAgP9j8gMAAPi9HTt2KCYmRt26dXNua9q0qbKysnTffffpq6++8ujkR0REhG688UZJ0qJFi/Tggw8qKSlJS5YsUWJiYr7v+89//qPk5GQlJCTojjvuUGJiosaMGaO5c+dqzpw5Kl++vEfKezWdOHFCOTk5ttSlZs2aatKkSaExn3/+uQYPHqzOnTvrn//8p0JDQ52vtW7dWo8++qjef/99RUREuJV3p06dlJiYqLfffjvfyY+FCxfq3LlzevDBB91Kt6T57bffVLp0aW8XAz7in//8pxo1aqSqVat6LA/63NV35MgRRUZGKjo6Os9rL7zwgsqWLavU1FTnfmnbtq1q1KihESNG5LsCpKhz90cffaTVq1c7JzEkqVWrVvrpp5/01FNP6e6771ZQUJDbsQAAIDBw2ysAAOD3tm/frgYNGuTZfujQIUlS7dq13U7z1Vdf1fLly93Ke8SIEerTp4/uvfderV+/vsCJD0mKjY3Vyy+/rNTUVFWvXl3NmjXTzp071bt3b8XExBT4vo0bN6p9+/aKiYlRbGysOnfurO++++6K46zq2rWrGjVqpFmzZql+/fqKiIhQ5cqVlZKSopycnHzfs2vXLlWsWFGdOnXSu+++q9OnT19x/laMHTtWQUFBeuONN1wmPi7Vq1evPPvlu+++U9++fVWhQgWFhYWpdu3aeu2115yvBwUFqV+/ftq+fbv+85//5EnznXfecdbT03JvI/TVV1+pR48eio6OVkxMjO677z4dP37cJfb48eN6+OGHVblyZYWFhemaa65Rs2bNtGbNGmdaTz31lCSpevXqzlvSfPrpp858duzYoZ49eyo2NtZltUxRbSZJ33//vQYMGKCaNWuqdOnSqlSpkrp27ZqnDXPz2rVrl3r16qWYmBjFxcVp2LBhysrK0t69e9WxY0dFRUWpWrVqmjBhgu1tlevo0aPq06ePYmJiFB8frwceeEAZGRkuMRs3blSbNm0UFRWl0qVL6/bbb9fKlStd8i2oXa2mcWn5v/nmmyLLVNS+zs8333wjh8Oh999/37lt+/btcjgcqlOnjktst27dXFa3GWO0bNky/elPfyow/UsdPHiwyH1gR5/zVF+SpOXLl8vhcOiTTz7J89rrr7/uzLew/X/+/HndfPPNuv766132YVpamhISEtSyZUtlZ2dbKk9Bt5RyOBz68ccfLddL+v02jW+99Zbatm2ra6+9Vj/88EO+cZ9//rlatmzpMiEVFRWlP/7xj9q0aZOOHDniVr6StGzZMpUpU0a9evVy2T5gwAAdPnxYX3zxxRXFFrcveLIvAQAA+zD5AQAA/Nqvv/6qAwcOqH79+srKylJWVpaOHTumuXPnasyYMXrooYd02223uZ3utm3b1Lt370InQHLzrlKlitq3b6/p06dr1qxZ+sc//lHgl++5rr32WnXp0sVlW+6X7CEhIfm+Z9SoUWrRooUqV66shQsX6s0339TBgwfVpk0bnTlzxu04d2zfvl3/93//p1dffVVPPfWUPvjgAzVv3lwvvfSS3n777Xzf06RJE82ZM0chISEaOHCgKlSooF69emnp0qW6cOGCW/k/+uijCg4OVnR0tDp06KCNGze6vJ6dna1169apUaNGqlixouV09+zZo1tvvVW7d+/W5MmTtWLFCnXu3FlDhgzR6NGjnXEPPPCAHA5Hnrru2bNHX375pfr163dVf3HcvXt3XX/99frnP/+pUaNGafny5erQoYMuXrzojElOTtby5cv14osvatWqVXrzzTfVtm1b/frrr5Kkhx56SI8//rgkaenSpdq8ebM2b96sW265xZlGjx49dP311+v999/XzJkznXW20maHDx9WuXLlNH78eKWmpuq1115TcHCwGjdurL179+apU+/evVW/fn0tWbJEAwcO1Kuvvqonn3xSd911lzp37qxly5apdevWevrpp7V06VJb2yrXn/70J91www1asmSJnnnmGS1YsEBPPvmk8/X169erdevWysjI0FtvvaWFCxcqKipKXbt21eLFiy21q5U03CmTVPS+zk+dOnVUsWJFlwmSNWvWKCIiQnv27NHhw4clSVlZWVq/fr3atm3rjMv9otvq5Ic7+6A4fS6XJ/pSly5dVKFCBb3zzjt5Xps9e7ZuueUWJSUlFbr/w8PD9d577+nYsWN64IEHJEk5OTm69957ZYzRwoULLZ9HctPN/Vu7dq0qVaqkhIQExcXFFfn+3377TYsXL9add96phIQEPf744ypbtqwWL16sm266Kd/3ZGZmKiwsLM/23G35TQ4Xde7evXu3ateureBg15tW5K7W3L179xXF5ipuX/DUeQkAANjEAAAA+LFVq1YZSXn+goODzcsvv3zF6WZlZZm+ffuakJAQs2zZsiLzDg8PN1u2bLni/Iry4YcfGklmwoQJLtv37dtnJJl58+a5FeeOQ4cOGUmmRo0a5uTJk87tmZmZJiEhwXTp0qXINNLT083bb79t2rdvb4KDg01MTIzp37+/+fjjj01WVlaB79uxY4d54oknzLJly8yGDRvM22+/bWrXrm2CgoJMamqqMy4tLc1IMvfcc0+eNLKysszFixedfzk5Oc7XOnToYK699lqTkZHh8p7HHnvMhIeHmxMnTji3tWjRwpQvX95kZmY6tw0fPtxIMvv27SuyDfKTkpJiJJnjx4/n+3q "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(0,1,70), np.linspace(0,1.5e5,70)), 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(0,1,70), np.linspace(0,1.5e5,70)), 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": 12, "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(0,1,70), np.linspace(0,1.5e5,70)), 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\"$E_0$\")\n", "ax0.set_title(\"found energyloss wrt electron energy\")\n", "\n", "a1=ax1.hist2d(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,70)), 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\"$E_0$\")\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": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n", "#upstream\n", "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(0,1,80), np.linspace(0,1.5e5,80)), 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(0,1,80), np.linspace(0,1.5e5,80)), 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": 14, "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(0,1,80), np.linspace(0,1.5e5,80)), 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(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,1.5e5,80)), 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, Downstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "env1", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2}
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