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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": [ "44804" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "file = uproot.open(\n", " \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_rad_length_beginVelo2endUT.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\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)\n", " & (~allcolumns.lost)\n", " & (allcolumns.fromB)\n", " & (allcolumns.p > 5e3)] # B: 9056\n", "lost = allcolumns[(allcolumns.isElectron)\n", " & (allcolumns.lost)\n", " & (allcolumns.fromB)\n", " & (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 = 0.8343228283189001 +/- 0.0017564670414882176\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", "\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", " cut_brem_found.end_record()\n", "\n", "cut_brem_found = ak.Array(cut_brem_found)\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", " 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)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 3.26e+04,\n", " brem_photons_pe: [824, 287, 1.26e+04, 4.49e+03, 3.59e+03, 111],\n", " brem_vtx_z: [157, 158, 601, 2.33e+03, 8.65e+03, 8.67e+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: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>" ] }, "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]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### in magnet\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "inmagnet_found = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(cut_brem_found, axis=0)):\n", "\n", " inmagnet_found.begin_record()\n", " inmagnet_found.field(\"energy\").real(cut_brem_found[itr, \"energy\"])\n", "\n", " inmagnet_found.field(\"brem_photons_pe\")\n", " inmagnet_found.begin_list()\n", " for jentry in range(cut_length_found[itr]):\n", " if cut_brem_found[itr, \"brem_vtx_z\", jentry] > 1500:\n", " if cut_brem_found[itr, \"brem_vtx_z\", jentry] <= 9500:\n", " inmagnet_found.real(cut_brem_found[itr, \"brem_photons_pe\",\n", " jentry])\n", " else:\n", " continue\n", " inmagnet_found.end_list()\n", "\n", " inmagnet_found.field(\"brem_vtx_z\")\n", " inmagnet_found.begin_list()\n", " for jentry in range(cut_length_found[itr]):\n", " if cut_brem_found[itr, \"brem_vtx_z\", jentry] > 1500:\n", " if cut_brem_found[itr, \"brem_vtx_z\", jentry] <= 9500:\n", " inmagnet_found.real(cut_brem_found[itr, \"brem_vtx_z\", jentry])\n", " else:\n", " continue\n", " inmagnet_found.end_list()\n", " inmagnet_found.end_record()\n", "\n", "inmagnet_found = ak.Array(inmagnet_found)\n", "\n", "inmagnet_lost = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(cut_brem_lost, axis=0)):\n", "\n", " inmagnet_lost.begin_record()\n", " inmagnet_lost.field(\"energy\").real(cut_brem_lost[itr, \"energy\"])\n", "\n", " inmagnet_lost.field(\"brem_photons_pe\")\n", " inmagnet_lost.begin_list()\n", " for jentry in range(cut_length_lost[itr]):\n", " if cut_brem_lost[itr, \"brem_vtx_z\", jentry] > 1500:\n", " if cut_brem_lost[itr, \"brem_vtx_z\", jentry] <= 9500:\n", " inmagnet_lost.real(cut_brem_lost[itr, \"brem_photons_pe\",\n", " jentry])\n", " else:\n", " continue\n", " inmagnet_lost.end_list()\n", "\n", " inmagnet_lost.field(\"brem_vtx_z\")\n", " inmagnet_lost.begin_list()\n", " for jentry in range(cut_length_lost[itr]):\n", " if cut_brem_lost[itr, \"brem_vtx_z\", jentry] > 1500:\n", " if cut_brem_lost[itr, \"brem_vtx_z\", jentry] <= 9500:\n", " inmagnet_lost.real(cut_brem_lost[itr, \"brem_vtx_z\", jentry])\n", " else:\n", " continue\n", " inmagnet_lost.end_list()\n", " inmagnet_lost.end_record()\n", "\n", "inmagnet_lost = ak.Array(inmagnet_lost)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "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", "inmagnet_brem_found = inmagnet_found[ak.sum(inmagnet_found[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) >= cutoff_energy]\n", "magnet_energy_found = ak.to_numpy(inmagnet_brem_found[\"energy\"])\n", "magnet_eph_found = ak.to_numpy(\n", " ak.sum(inmagnet_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "magnet_residual_found = magnet_energy_found - magnet_eph_found\n", "magnet_energyloss_found = magnet_eph_found / magnet_energy_found\n", "\n", "inmagnet_brem_lost = inmagnet_lost[ak.sum(inmagnet_lost[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) >= cutoff_energy]\n", "magnet_energy_lost = ak.to_numpy(inmagnet_brem_lost[\"energy\"])\n", "magnet_eph_lost = ak.to_numpy(\n", " ak.sum(inmagnet_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "magnet_residual_lost = magnet_energy_lost - magnet_eph_lost\n", "magnet_energyloss_lost = magnet_eph_lost / magnet_energy_lost" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20316.361014308728" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.mean(magnet_eph_lost)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " magnet_energyloss_found,\n", " alpha=0.5,\n", " bins=80,\n", " density=True,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"found\",\n", ")\n", "plt.hist(\n", " magnet_energyloss_lost,\n", " alpha=0.5,\n", " bins=80,\n", " density=True,\n", " histtype=\"bar\",\n", " color=\"darkorange\",\n", " label=\"lost\",\n", ")\n", "\n", "# plt.vlines(ak.mean(both_eloss),0,3,colors=\"red\", label=\"mean\")\n", "plt.xlabel(r\"Energyloss Ratio $E_\\gamma/E_0$\")\n", "plt.ylabel(\"counts (normed)\")\n", "plt.title(\n", " r\"$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\"\n", ")\n", "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAHhCAYAAAB5kCQCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABSeElEQVR4nO3dT4zb6J03+G/NDFJjbMFmlXMZ7KVNJYfJZWyqfHizL+AkJtPndEkuYCeLObwpKY26NdJiajCA7cMbNRVfC2mqehazQLCAi3IP9pghPdMe7A5erEts55IBJhHtHN6ZPaRL7EINPJU3ae6h+mHrD6WiKEoiVd8PINglUuTDhxT508Pn+XElCIIARERERDn2B4suABEREdG0GNAQERFR7jGgISIiotxjQENERES5x4CGiIiIco8BDREREeUeAxoiIiLKvT9adAFmqVqtolAo4JNPPsHt27dRKpUWXaSZu4zbTEREtLQBTblchizLqNVqAIBisQhJkqCq6oJLNjuXcZuJiIgAYGUZMwV7nodCoYBOpwNZlgEAjUYDtm3Dtu0Fl242ZrXNjuNgY2MDiqKkVVQiIqLULWUfGtd1ASC8sAOAoihwHAe+7y+oVLM1q232fR/Hx8fTFo+IiGimljKgef78OSRJ6ntvY2MDAJbi4qzrOjzP63tv2bd5mYjg87LwfR/NZnPomKXpeZ6HZrO5tD/UKL7Ldl6JkpuAxvd9NBoNrKysYGVlBevr6yiXy9A0DYVCAY1Go29ecTEflJWTarPZRKFQCLdH0zQ4jgPg/MDUNC2cJrat2WwCAK5fvx7OJz6Th22+jMrlcrgfxUvX9ch5XddFuVxGoVDA+vo6isUiNE2DrutoNpsoFosTrdtxnL5jrFgsotVqDc3XarVQLBbDedI8MTabTdy4cQPVapUX3QSq1erI/d5oNFAoFFCtVvmjZQl4nodqtYpqtYpyuTz2OxP3vKLrOsrlcnitHHUtiDtf5gU5oyhKACAwTTN8z7KsAEBQKpWCIAgCwzACSZL6PtdutwMAQafTmWt5xzEMIwAQyLI8NE1V1UBRlKDb7YbvtdvtoFKpBJVKJTAMI6jVauH0WW2zZVmBbduJP3+ZdbvdQJbloFQq9b3a7fbQvLVaLTyGe/dXt9sNKpVKACBI8nXtdrvhZ3u/M4NM0xw6ftIiti1qu6fR6XT6vh/LSJKksfutVCpl7rw2a8u438W5uvc7UqvVAkmShvZt3POKoijhNTEIgsC27UCSpMTz5UHuRjmJyLF35I4Ymix+fcqyPBTZil8wvX1MFq3T6QDAUGQtRisNduZVFAXFYhGWZUGSJGxvb4e3mdLa5mq12vf30dERNjY2YFlW3/umacZeJnD+S73dbo+dR9O0pRpmXq/XYRjGhdtULpfRarVgGEY4Qk2QJAmmaaJYLA7tmzgkSUKtVkOj0YBlWahUKpHztdtt7O3tTbz8OESLYtrK5XL4XVhGov/bvXv3Rs4zqlV2mS3jft/Z2YGiKH2DLwzDQKPRgK7rfeffOOcVXdfhui6ePn0avqeqKmRZxs7OTngujjtfbiw6opqEiGKjWjTw+a/QbrcbdDqdoV8thmEEiqKkWh7DMKb6ZSTLcl85O51OoCjKyF9kpVIpsG07MAwj6Ha7Qa1WC+ed1TZfhhaabrc7tt6TkiQpMAxj7C8d0boYZz8l3Ze9rTSjyiJJ0sx+9YqWyDR/8V2GlolSqXThPhetd8tcD73ytt8tywoURRlbXnHurlQqQ9NUVR3a3jjnFUmSIq+Tg9/FuPPlRa4CGlHJgzs+6qJQKpUCwzDCvxVFSf3CbFlW5EEYh7jIiGZ+y7ICWZZjHUC1Wi1yvlls82UIaIIgCANEWZb76jApcayKlyRJgWVZQ/OJoDZOMBV1Umy320GpVApUVQ1kWQ5qtVrkZ8WFoLdpWbAsK/L9cbrdbmCaZniMif9LkhSUSqW+4EjURe98o8oibrHVarVAVdVAVdWh4098VwAEqqoONbdftIzesovje1SZepdVqVQCWZYv3Fe2bYf7XSyv3W6HZVYUpa+8oy7ScY4LEdCI29GSJAWSJEWeI0U9iNuLvfOMO45EfamqGpimGXQ6nUBV1UCSpEBV1b7b3rIsB5IkjTwOo7Tb7UCSpLBueutB3K40DGPkfhflEXVuWVbQ7XbDMk7yfZZlOVx2pVIJ982kF3fTNANZloNKpXLhDwVx/YqqM7F/xbkjznlF/PCP+n6JY1NcQ+LMFwTTHwOzPoaEXAU04qDt3YG9wczgCaG3r0nav76FqOg2DtM0+76YvTv1IuOi/bjbLIKxWq029hfEpAFN3OWmTZywxRf8ogvnOOJL1dtHaVLdbjewLCsMksQJqPciIn6ZiYv9pNrtdqCqavi3+C5EBdm96xrcpiSBb2+/HlVVg1qtFgZGg62o4iSsqmr4y1J8vvdiIy5svRcO8T0ZvCiJC93g8RVnGZ1OJyynKPuoMpVKpb4Tq2masS6Qony99Tp4kehd5uB7Yl9edPyJMquqGlQqlb7jXhwbvYFA73dT/AC86DjqdDp96xH1JbZHrNu27b66nSQAGOwHKYgLnzBqvwfBcIu3qqoTtzAMfndG/YgeJcm5Q9Rj1HmqN6ALgnjnFXG8R5W5N4iJO18QTH8MzOMYCoKcBTRi54kvo4hQF9mCIC6kkxI7TPwySaNVIC7xy0MY1/FwkoBmkuXOgvjC9LZgiRNCkmh/kl9ZFxEn7N6LXO8v+VHLb7fbgWEYfS/x+cFf+kEQhMdT1PLED4Leuuh0Ook7A4uT/eA+HvzhMWq+3ouu2J6o75L4rkf9ch+8sMVdhtgfg9+7wTJF/VKM810VAeTgRSqqiX+wI7h4L855ZfAXvCAuduJYEfsg6nsQ5zgSF7jBi1/UvhHzTnpOE2XuJVpmhHEBzeDFOsl3frAlT5yjx33/p23d7b0lPEhs77jz8+B5Zdy+Fseloiix5xOmPQbmcQzlZti2GJ6sKAra7Xb4AtA35HneSqUSNE2DpmkTDUsV5RWdsXRdn8uwVk3TcHh4iIODAwDnHal93x/5eARVVWM9OmHS5c7C0dERAGB7ezvsXLe9vQ0g2dD1SqWCTqcDTdNw9+5dVKvVxMMZS6VS2Ml7kg7ViqKgUqlA1/Ww87iqqvA8D67rol6vh8Mty+Vy+DlRF73E58Xwf+C84+G0nYEHO52LzsuDndo3Nzf7/pYkKaxPsT1RGanF8i6qtyTLiOpY2jsEWpZlNBqNvrQQgx23o8iyDEVRIofJe54Xbrfv+/B9f6gOW61W3/68yOA2D+4DsZ23b98eKsskx9FgfYly93ZOFu+JQQ9xiTL31tnjx49jDxQQ3xXRwd4wjInWD/QPNtnZ2QFw/h2JOk7EMOtisRhmaY9zbAySJCnsrN9b947jhN/VUQM7os4rcTpL+74fe76o8vaa9BiY5TGUm4BG7LTeA05RlPACOsmXP4p4qGOSl2macBwH6+vrsQIr13XDk5iiKOGXYFR+krQ0m004joODgwNIkoRmswld12Hb9sgvTJyDPslyZ8F1XZRKpb6Tu7hwTDMapFQqod1u953wkwSfqqpCUZSwTL0X+HGBkiRJ4TaJf0WuGMuy+l7dbhdBEEQGkmL0gkh0BwCHh4cjRz4lJco4SfA3LveNqKeLlpfGMgaJ0TS6rqNQKEyUo2fwAt1sNsMRS+Lic3h4OHTuEvOPG910EbH/B7d38Puc5DiaFXEcirpxXXcoCL5IkiAmiuM4aLVaYZA0qDdnS6fTmfo7ZJomDMOA67phPijP88Lz1rh6GDyviHNu1DlKvCfLcuz58iQ3AY0IFDRN63tffOHEL52kTNNEp9NJ9DJNE4qiwLbtWCcAsS1iXvELYNbZVMUJ1vO8MHjqdDpTn7RmtdxJiIvA4NDm58+fA5g+4O01zXEmy3J4kuq9uES1qEQRn+1t2ZiE2D+GYaDVamFzczP14a+ijElOhuN+EcYNStNYhiDLMl6+fBm2ihWLxb4WrnEGL9DioqWqargM0zSHLoaPHz+GqqpT7ZfedA7jJD2OZkGSJJRKJTiOA8/z8Pj "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nstart = 0\n", "nend = 5e4\n", "plt.hist(\n", " magnet_residual_found,\n", " alpha=0.5,\n", " bins=70,\n", " density=True,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"found\",\n", " range=[nstart, nend],\n", ")\n", "plt.hist(\n", " magnet_residual_lost,\n", " alpha=0.5,\n", " bins=70,\n", " density=True,\n", " histtype=\"bar\",\n", " color=\"darkorange\",\n", " label=\"lost\",\n", " range=[nstart, nend],\n", ")\n", "\n", "# plt.vlines(ak.mean(both_eloss),0,3,colors=\"red\", label=\"mean\")\n", "# plt.xlim(0,50000)\n", "plt.xlabel(r\"Residual Energy in magnet $E_\\gamma$\")\n", "plt.ylabel(\"counts (normed)\")\n", "plt.title(\n", " r\"$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\"\n", ")\n", "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cut_length_found[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split in Upstream and Downstream Events and analyse separately\n" ] }, { "cell_type": "code", "execution_count": 13, "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\",\n", " 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\",\n", " 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", "upstream_found = ak.Array(upstream_found)\n", "downstream_found = ak.Array(downstream_found)\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\",\n", " 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", "upstream_lost = ak.Array(upstream_lost)\n", "downstream_lost = ak.Array(downstream_lost)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 1.28e+04,\n", " brem_photons_pe: [7.42e+03],\n", " brem_vtx_z: [35.6]}\n", "-----------------------------------\n", "type: {\n", " energy: float64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Record {energy: 1.28e+04, ...} type='{energy: float64, brem_photons_pe: va...'>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upstream_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "upstream: cutoff energy = 350MeV, sample size: 6604\n", "eff = 0.8798 +/- 0.004\n" ] } ], "source": [ "# plot efficiency against cutoff energy\n", "up_efficiencies = []\n", "up_deff = []\n", "\n", "for cutoff_energy in range(0, 10050, 200):\n", " up_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) < cutoff_energy]\n", " up_nobrem_l = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) < cutoff_energy]\n", "\n", " if ak.num(up_nobrem_f, axis=0) + ak.num(up_nobrem_l, axis=0) == 0:\n", " up_efficiencies.append(0)\n", " up_deff.append(0)\n", " continue\n", "\n", " eff = t_eff(up_nobrem_f, up_nobrem_l)\n", " deff = eff_err(up_nobrem_f, up_nobrem_l)\n", " up_efficiencies.append(eff)\n", " up_deff.append(deff)\n", "\n", " # print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n", " # print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(up_nobrem_f, up_nobrem_l),4))\n", "\"\"\"\n", "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n", "\"\"\"\n", "cutoff_energy = 350.0 # MeV\n", "\"\"\"\n", "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n", "\"\"\"\n", "up_nobrem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) < cutoff_energy]\n", "up_nobrem_lost = upstream_lost[ak.sum(\n", " upstream_lost[\"brem_photons_pe\"], axis=-1, keepdims=False) < cutoff_energy]\n", "\n", "print(\n", " \"\\nupstream: cutoff energy = 350MeV, sample size:\",\n", " ak.num(up_nobrem_found, axis=0) + ak.num(up_nobrem_lost, axis=0),\n", ")\n", "print(\n", " \"eff = \",\n", " np.round(t_eff(up_nobrem_found, up_nobrem_lost), 4),\n", " \"+/-\",\n", " np.round(eff_err(up_nobrem_found, up_nobrem_lost), 3),\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nobrem_vertices\n", "upstream: cutoff energy = 350MeV, sample size: 6604\n", "eff = 0.8798 +/- 0.004\n", "\n", "downstream: cutoff energy = 350MeV, sample size: 21986\n", "eff = 0.8739 +/- 0.002\n" ] } ], "source": [ "down_efficiencies = []\n", "down_deff = []\n", "\n", "for cutoff_energy in range(0, 10050, 200):\n", " down_nobrem_f = downstream_found[ak.sum(\n", " downstream_found[\"brem_photons_pe\"], axis=-1, keepdims=False) <\n", " cutoff_energy]\n", " down_nobrem_l = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) < cutoff_energy]\n", "\n", " if ak.num(down_nobrem_f, axis=0) + ak.num(down_nobrem_l, axis=0) == 0:\n", " down_efficiencies.append(0)\n", " down_deff.append(0)\n", " continue\n", " eff = t_eff(down_nobrem_f, down_nobrem_l)\n", " deff = eff_err(down_nobrem_f, down_nobrem_l)\n", " down_efficiencies.append(eff)\n", " down_deff.append(deff)\n", "\n", " # print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n", " # print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(down_nobrem_f, down_nobrem_l),4))\n", "\"\"\"\n", "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n", "\"\"\"\n", "cutoff_energy = 350.0 # MeV\n", "\"\"\"\n", "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n", "\"\"\"\n", "down_nobrem_found = downstream_found[ak.sum(\n", " downstream_found[\"brem_photons_pe\"], axis=-1, keepdims=False) <\n", " cutoff_energy]\n", "down_nobrem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) < cutoff_energy]\n", "\n", "print(\n", " \"nobrem_vertices\\nupstream: cutoff energy = 350MeV, sample size:\",\n", " ak.num(up_nobrem_found, axis=0) + ak.num(up_nobrem_lost, axis=0),\n", ")\n", "print(\n", " \"eff = \",\n", " np.round(t_eff(up_nobrem_found, up_nobrem_lost), 4),\n", " \"+/-\",\n", " np.round(eff_err(up_nobrem_found, up_nobrem_lost), 3),\n", ")\n", "\n", "print(\n", " \"\\ndownstream: cutoff energy = 350MeV, sample size:\",\n", " ak.num(down_nobrem_found, axis=0) + ak.num(down_nobrem_lost, axis=0),\n", ")\n", "print(\n", " \"eff = \",\n", " np.round(t_eff(down_nobrem_found, down_nobrem_lost), 4),\n", " \"+/-\",\n", " np.round(eff_err(down_nobrem_found, down_nobrem_lost), 3),\n", ")" ] }, { "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": [ "# 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(\n", " xlabel=\"cutoff energy [MeV]\",\n", " ylabel=r\"$\\epsilon$\",\n", " title=\"upstream\",\n", " ylim=[0.8, 1.0],\n", ")\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_,\n", " down_efficiencies,\n", " yerr=down_deff,\n", " ls=\"\",\n", " capsize=1,\n", " fmt=\".\")\n", "ax[1].set(\n", " xlabel=\"cutoff energy [MeV]\",\n", " ylabel=r\"$\\epsilon$\",\n", " title=\"downstream\",\n", " ylim=[0.8, 1.0],\n", ")\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(\n", " r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "brem vertices\n", "upstream eff = 0.826 +/- 0.002\n", "downstream eff = 0.796 +/- 0.003\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\"],\n", " axis=-1,\n", " keepdims=False) >= cutoff_energy]\n", "up_energy_found = ak.to_numpy(upstream_brem_found[\"energy\"])\n", "up_eph_found = ak.to_numpy(\n", " 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", "upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],\n", " axis=-1,\n", " keepdims=False) >= cutoff_energy]\n", "up_energy_lost = ak.to_numpy(upstream_brem_lost[\"energy\"])\n", "up_eph_lost = ak.to_numpy(\n", " 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", "print(\n", " \"brem vertices\\nupstream eff = \",\n", " np.round(t_eff(upstream_brem_found, upstream_brem_lost), 3),\n", " \"+/-\",\n", " np.round(eff_err(upstream_brem_found, upstream_brem_lost), 3),\n", ")\n", "\n", "downstream_brem_found = downstream_found[ak.sum(\n", " downstream_found[\"brem_photons_pe\"], axis=-1, keepdims=False) >=\n", " cutoff_energy]\n", "down_energy_found = ak.to_numpy(downstream_brem_found[\"energy\"])\n", "down_eph_found = ak.to_numpy(\n", " 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", "downstream_brem_lost = downstream_lost[ak.sum(\n", " downstream_lost[\"brem_photons_pe\"], axis=-1, keepdims=False) >=\n", " cutoff_energy]\n", "down_energy_lost = ak.to_numpy(downstream_brem_lost[\"energy\"])\n", "down_eph_lost = ak.to_numpy(\n", " 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", "print(\n", " \"downstream eff = \",\n", " np.round(t_eff(downstream_brem_found, downstream_brem_lost), 3),\n", " \"+/-\",\n", " np.round(eff_err(downstream_brem_found, downstream_brem_lost), 3),\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upstream:\n", "mean energyloss relative to initial energy (found): 0.3289231319498724\n", "mean energyloss relative to initial energy (lost): 0.530926023218989\n", "downstream:\n", "mean energyloss relative to initial energy (found): 0.18366915850891907\n", "mean energyloss relative to initial energy (lost): 0.32907909342930114\n" ] } ], "source": [ "print(\n", " \"upstream:\\nmean energyloss relative to initial energy (found): \",\n", " ak.mean(up_energyloss_found),\n", ")\n", "print(\"mean energyloss relative to initial energy (lost): \",\n", " ak.mean(up_energyloss_lost))\n", "\n", "print(\n", " \"downstream:\\nmean energyloss relative to initial energy (found): \",\n", " ak.mean(down_energyloss_found),\n", ")\n", "print(\"mean energyloss relative to initial energy (lost): \",\n", " ak.mean(down_energyloss_lost))" ] }, { "cell_type": "code", "execution_count": 20, "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", "ax[0].hist(\n", " up_energyloss_lost,\n", " bins=100,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"darkorange\",\n", " label=\"lost\",\n", ")\n", "ax[0].hist(\n", " up_energyloss_found,\n", " bins=100,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"found\",\n", ")\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(\n", " down_energyloss_lost,\n", " bins=100,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"darkorange\",\n", " label=\"lost\",\n", ")\n", "ax[1].hist(\n", " down_energyloss_found,\n", " bins=100,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"found\",\n", ")\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", "most electrons lose little energy relative to their initial energy downstream\n", "\"\"\"\n", "fig.suptitle(\n", " r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " up_energyloss_lost,\n", " bins=100,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"darkorange\",\n", " label=\"lost\",\n", ")\n", "plt.hist(\n", " up_energyloss_found,\n", " bins=100,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"found\",\n", ")\n", "plt.xlabel(r\"$E_\\gamma/E_0$\")\n", "plt.ylabel(\"counts (normed)\")\n", "plt.title(\n", " r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n", "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n", "plt.text(0.35, 2.0, \"Upstream\", size=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "both_eloss = np.append(up_energyloss_found, up_energyloss_lost)\n", "plt.hist(\n", " both_eloss,\n", " bins=100,\n", " density=True,\n", " histtype=\"bar\",\n", " color=\"cornflowerblue\",\n", " label=\"Upstream\",\n", ")\n", "plt.vlines(ak.mean(both_eloss), 0, 3, colors=\"red\", label=\"mean\")\n", "plt.xlabel(r\"Energyloss Ratio $E_\\gamma/E_0$\")\n", "plt.ylabel(\"counts (normed)\")\n", "plt.title(\n", " r\"$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\"\n", ")\n", "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "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(\n", " up_energyloss_found,\n", " up_energy_found,\n", " bins=(np.linspace(0, 1, 80), np.linspace(0, 5e4, 80)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=15,\n", ")\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(\n", " up_energyloss_lost,\n", " up_energy_lost,\n", " bins=(np.linspace(0, 1, 50), np.linspace(0, 5e4, 50)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=15,\n", ")\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(\n", " 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": 24, "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(\n", " down_energyloss_found,\n", " down_energy_found,\n", " bins=(np.linspace(0, 1, 80), np.linspace(0, 5e4, 80)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=15,\n", ")\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(\n", " down_energyloss_lost,\n", " down_energy_lost,\n", " bins=(np.linspace(0, 1, 50), np.linspace(0, 5e4, 50)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=15,\n", ")\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(\n", " 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": 25, "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", "nbins = 60\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 6))\n", "\n", "a0 = ax0.hist2d(\n", " up_energyloss_found,\n", " up_residual_found,\n", " bins=(np.linspace(0, 1, nbins), np.linspace(0, 5e4, nbins)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=20,\n", ")\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(\n", " up_energyloss_lost,\n", " up_residual_lost,\n", " bins=(np.linspace(0, 1, nbins), np.linspace(0, 5e4, nbins)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=20,\n", ")\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(\n", " 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": 26, "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(\n", " down_energyloss_found,\n", " down_residual_found,\n", " bins=(np.linspace(0, 1, 60), np.linspace(0, 5e4, 60)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=25,\n", ")\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(\n", " down_energyloss_lost,\n", " down_residual_lost,\n", " bins=(np.linspace(0, 1, 60), np.linspace(0, 5e4, 60)),\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=20,\n", ")\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(\n", " 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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2}
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