{ "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": "code", "execution_count": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
{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",
       "}
" ], "text/plain": [ "" ] }, "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
{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",
       "}
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upstream_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "upstream: cutoff energy = 350MeV, sample size: 1562\n", "eff = 0.9181 +/- 0.007\n" ] } ], "source": [ "#plot efficiency against cutoff energy \n", "up_efficiencies = []\n", "up_deff = []\n", "\n", "\n", "for cutoff_energy in range(0,10050,200):\n", "\tup_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)" ] }, "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.arange(0,10050,step=200)\n", "\n", "ax[0].errorbar(x_,up_efficiencies,yerr=up_deff, ls=\"\", capsize=1,fmt=\".\")\n", "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.8,1.0])\n", "ax[0].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n", "ax[0].set_xticks(np.arange(0,10100,step=200),minor=True)\n", "ax[0].grid()\n", "\n", "ax[1].errorbar(x_,down_efficiencies,yerr=down_deff, ls=\"\", capsize=1,fmt=\".\")\n", "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.8,1.0])\n", "ax[1].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n", "ax[1].set_xticks(np.arange(0,10100,step=200),minor=True)\n", "ax[1].grid(True)\n", "\n", "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "brem vertices\n", "upstream eff = 0.851 +/- 0.004\n", "downstream eff = 0.836 +/- 0.005\n" ] } ], "source": [ "cutoff_energy=350\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.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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(\"brem vertices\\nupstream 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.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upstream:\n", "mean energyloss relative to initial energy (found): 0.33078325542598164\n", "mean energyloss relative to initial energy (lost): 0.5708618852236069\n", "downstream:\n", "mean energyloss relative to initial energy (found): 0.19104090843883118\n", "mean energyloss relative to initial energy (lost): 0.3051594568487781\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": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "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, photons w/ brem_vtx_z$<9500$mm\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "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(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax0.set_ylim(0,5e4)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0$\")\n", "ax0.set_title(\"found energyloss wrt electron energy\")\n", "\n", "a1=ax1.hist2d(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax1.set_ylim(0,5e4)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0$\")\n", "ax1.set_title(\"lost energyloss wrt electron energy\")\n", "\n", "fig.colorbar(a1[3],ax=ax1)\n", "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "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(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax0.set_ylim(0,5e4)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0$\")\n", "ax0.set_title(\"found energyloss wrt electron energy\")\n", "\n", "a1=ax1.hist2d(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax1.set_ylim(0,5e4)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0$\")\n", "ax1.set_title(\"lost energyloss wrt electron energy\")\n", "\n", "fig.colorbar(a1[3],ax=ax1)\n", "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "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(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\n", "ax0.set_ylim(0,5e4)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\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(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \n", "ax1.set_ylim(0,5e4)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\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": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "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(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\n", "ax0.set_ylim(0,5e4)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\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(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \n", "ax1.set_ylim(0,5e4)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\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 }