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{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import uproot\n", "import numpy as np\n", "import sys\n", "import os\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits import mplot3d\n", "import itertools\n", "import awkward as ak\n", "import seaborn as sns\n", "from scipy.optimize import curve_fit\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "\n", "#look at particles only from Signal\n", "allcolumns = file.arrays()\n", "tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)]\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] \n", "\n", "#ak.num(tracked, axis=0)\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#lost\n", "l_eph = lost[\"brem_photons_pe\"]\n", "ak.nan_to_num(l_eph)\n", "l_pT = lost[\"pt\"]\n", "l_sci_x = lost[\"scifi_hit_pos_x\"]\n", "ak.nan_to_num(l_sci_x)\n", "\n", "#found\n", "f_eph = tracked[\"brem_photons_pe\"]\n", "ak.nan_to_num(f_eph)\n", "f_pT = tracked[\"pt\"]\n", "f_sci_x = tracked[\"scifi_hit_pos_x\"]\n", "ak.nan_to_num(f_sci_x)\n", "\n", "l_sci_x, l_pT = ak.broadcast_arrays(l_sci_x, l_pT)\n", "f_sci_x, f_pT = ak.broadcast_arrays(f_sci_x, f_pT)\n", "\n", "l_sci_x = ak.to_numpy(ak.flatten(l_sci_x))\n", "l_pT = ak.to_numpy(ak.flatten(l_pT))\n", "f_sci_x = ak.to_numpy(ak.flatten(f_sci_x))\n", "f_pT = ak.to_numpy(ak.flatten(f_pT))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABiwAAAIhCAYAAADdMJK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydeXgVRfb+38sSCFsgBEKCGFARF2QRFNwGcEMQlcUNEYmDy6iMI+A6DgIyyqACOjIu89MhLoiOXwgqAooLqMMiQdHBnRlwUHZkEZS9fn/kdvfp3Kp09829uU3u+3mePKlUV1edrl7Sde85540opRQIIYQQQgghhBBCCCGEEEJSSLVUG0AIIYQQQgghhBBCCCGEEMIvLAghhBBCCCGEEEIIIYQQknL4hQUhhBBCCCGEEEIIIYQQQlIOv7AghBBCCCGEEEIIIYQQQkjK4RcWhBBCCCGEEEIIIYQQQghJOfzCghBCCCGEEEIIIYQQQgghKYdfWBBCCCGEEEIIIYQQQgghJOXwCwtCCCGEEEIIIYQQQgghhKQcfmFBCCGEEEIIIYQQQgghhJCUUyW+sCgqKkIkEsGaNWuS0v8TTzyBoqKihPdbWFiIli1bJrxfizlz5mDMmDFJ6/9wJBKJuObkyy+/xJgxY7TXTrLPT0VYsGABIpEIFixYkLA+Tde5Ndb//d//JWysZGKam8cffxzHHHMMMjIyEIlEsH37dt/nOMzXQipZs2YNIpGI5/OxvPusIpTXb/fu3dG2bduEjteyZUsUFhbGvf8vv/yCMWPGJPS+JcSLRYsWYcyYMdi+fXuqTSGEVAKvvPIKTjzxRGRmZiISiWDFihWpNknLmDFjEIlEEtZfsteDyXqXOZzp3r07unfvbv9d3ntOss9PRSm7Rqwo5a3DI5EIhg0blrCxko1ubt5991107twZdevWRSQSwaxZs3yf47BfC6nEz1ojWeuJ8vq1ntdbtmxJ2HiJWF8n63M6QkysW7cOY8aMCe27VVWjSnxhkWwO1wfhnDlzMHbs2FSbESoWL16M6667zv77yy+/xNixY7UvTKNGjUJxcXElWuefk08+GYsXL8bJJ5+csD4P1+u8LLq5WbFiBW699Vb06NED7733HhYvXoz69ev7PsdhvhYOB8q7z8LYr4ni4mKMGjUq7v1/+eUXjB07ll9YkEpl0aJFGDt2LL+wICQN2Lx5MwYPHoyjjz4a8+bNw+LFi3Hsscem2qwqQWW/cxwOPPHEE3jiiSfsv8t7z7nwwguxePFi5OXlVaKF/im7RqwoVWkdXnZulFK4/PLLUbNmTbz++utYvHgxunXr5vsch/1aCDvJWk9U9jolEevrqvL5BTl8WLduHcaOHcsvLCqJGqk2gIQDpRT27NmDzMzMVJuSVLp27eq77dFHH51ESypGgwYNAh1LOqGbmy+++AIAcP311+PUU0+16/2e48q4Fvbv349IJIIaNcL1WP7ll19Qp06dVJsRCjp27OjZJpHnsarPfbr83yGEkMri22+/xf79+3H11VejW7duqTYnranq/8MB4IQTTvDdtkmTJmjSpEkSrakYXFeZKTs369atw08//YR+/frhnHPOcW3zc44r41oI6ztmOjwX/OJnfZ3I8xjWtXYi4fVFqhyqCjB16lQFQK1evdpV/+yzz6p27dqpWrVqqUaNGqm+ffuqL7/80tXmP//5j7riiitUXl6eysjIUE2bNlVnn322+vTTT5VSShUUFCgArp+CgoJy7Tl06JD629/+ptq3b69q166tGjZsqAYMGKD+85//uNoNGTIkpi+/+yql1Ny5c9XZZ5+tGjRooDIzM9Vxxx2nHnzwQbvvsnbLOQKgbrnlFvXkk0+q4447TtWsWVM9+eSTSimlPvzwQ3X22WerevXqqczMTHXaaaep2bNna+f8vffeU7/73e9U48aNVXZ2turXr5/68ccfy50fy766deuqlStXqrPPPlvVqVNH5eTkqFtuuUXt3r3b1fbXX39Vd999t2rZsqWqWbOmys/PVzfffLPatm2bq927776runXrprKzs1Xt2rVVixYtVP/+/V39AVCjR492HUPZn6lTpxrPj19bCgoK1IUXXqjmzp2rOnbsqGrXrq3atGmjnn32WVe73bt3q5EjR6qWLVva12mnTp3USy+9VO78vf/++wqAev/992Pm9LvvvlO9evVSdevWVUcccYQaMWKE2rNnT7n9lXedW2O99NJL6o9//KPKy8tT9evXV+ecc476+uuvY/qaP3++Ovvss1X9+vVVZmamOv3009U777xT7vhKKXXw4EE1btw4deyxx6ratWurrKwsddJJJ6lHH33U1e6rr75SV155pWratKnKyMhQLVq0UIMHD7aPsezcdOvWLebYhgwZYs+Z1/1samfdQ88//7w67rjjVGZmpmrXrp164403PPuzbHz++efViBEjVH5+vopEIuqrr75SSvmfw1mzZqmTTjpJZWRkqFatWqlHH31UjR49WpV9tE+ZMkWdddZZqkmTJqpOnTqqbdu2asKECWrfvn2udt26dVMnnniiWrhwoTrttNNUZmamuuKKK5RSSv3444/qsssuU/Xq1VMNGjRQl19+uVq8eLHrntHhdZ8p5e9ZHbRf61g+/vhjdeaZZ6rMzEzVqlUrNX78eHXw4EFXXzt27LDvQ+u+/sMf/qB27drlaldQUGBfO0p5n0fJ6tWrtfZa/Vnnbfny5WrAgAGqYcOGqlmzZkoppZYtW6auuOIKVVBQoGrXrq0KCgrUlVdeqdasWaOdEz/P5fKel/v27VNNmjRRV199dcxxbNu2TdWuXVsNHz488PyV93/niSeeUO3atVN169ZV9erVU23atFH33HOPa//169erG264QTVv3lzVrFlTtWzZUo0ZM0bt378/xs6yWM/kN954Q3Xo0EHVrl1bHXfccfb9OnXqVHXcccepOnXqqFNOOUUtW7Yspo/XXntNde3aVWVmZqp69eqpc889Vy1atMjVxjqPn332mbr00ktVgwYNVKNGjdTw4cPV/v371ddff6169uyp6tWrpwoKCtSECRNixgk6n+U9gyx7yv5Yz0f5P7HsfMlr3bq23n33XXXdddep7OxsVb9+fTV48GC1a9cutX79enXZZZeprKws1axZMzVy5MiY5wshJLno3v27detmb/fzDDO9F+neLYK8B82ePVu1b99eZWRkqJYtW6qHH35Y26cJP+9FpvWg33eq8t4v/b5z6N6fvv/+ezVo0CDVpEkTlZGRoY477jj1yCOPuN5FrHeEhx9+WE2cOFG1bNlS1a1bV3Xt2lUtXrzYc34s+95++21VWFioGjVqpOrUqaP69OmjXUMmYo1sHbd1jXm951RkvR5kjePnfUJH2f+HFVnr+l2H+7l3vv32WzVw4EDX9TNlyhTP41FKqX/+85/q1FNPtT8raNWqlbr22mtdbbZt26ZGjBihWrVqpTIyMlSTJk1Ur169XO+ycm507xXWM8N0jsuiaxfkvV1Hee+Yfudw5cqV6rzzzlOZmZkqJydH3XzzzWr27Nkxa+63335bXXzxxap58+aqVq1a6uijj1Y33HCD2rx5s6u/8t7t9+3bp+644w6Vm5urMjMz1RlnnKGWLl0a8/5VFq/7TCl/n+UE7dc6lpUrV6orr7xSNWjQQDVt2lRde+21avv27a6+KvJZWHnnsSx+Pr/QrdE2bdqkbrrpJnX88cerunXrqiZNmqgePXqoDz74QDsnfp7LXs/LSy65RB155JHaa/nUU09VHTt2DDx/5f3f8fO52N69e9W4ceNUmzZtVEZGhsrJyVGFhYVq06ZN2vmWWM/kr776Sp1//vmqTp06qlmzZmr8+PFKKaUWL16szjjjDFWnTh3VunVrVVRUFNPHv//9b3XxxRerhg0bqlq1aqn27dvHtLPO47Rp09Sdd96pmjVrpurWrav69OmjNmzYoHbu3Kmuv/561bhxY9W4cWNVWFiofv75Z1cfQeezvGeQZU/ZH+v5KP8nlp0vea1b19ZDDz2k/vKXv9hr/G7duqlvvvlG7du3T911110qLy9PNWj "text/plain": [ "<Figure size 2000x600 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(l_sci_x, l_pT, bins=200, cmap=plt.cm.jet, cmin=0, range=[[-3000,3000],[0,2000]])\n", "ax0.set_xlabel(\"scifi x\")\n", "ax0.set_ylabel(r\"$p_T$\")\n", "ax0.set_title(\"lost electron positions in the scifi in regard to their transverse momentum\")\n", "plt.colorbar(a0[3],ax=ax0)\n", "\n", "a1=ax1.hist2d(f_sci_x, f_pT, bins=200, cmap=plt.cm.jet, cmin=0, range=[[-3000,3000],[0,2000]])\n", "ax1.set_xlabel(\"scifi x\")\n", "ax1.set_ylabel(r\"$p_T$\")\n", "ax1.set_title(\"found electron positions in the scifi in regard to their transverse momentum\")\n", "plt.colorbar(a1[3],ax=ax1)\n", "\n", "\"\"\"\n", "B:\n", "we can see that the lost electrons cover a wider spread in the x direction of the scifi tracker, most widely scattered electrons have low pt\n", "\n", "D:\n", "heatmaps look fairly similar. lost e are more densely located between x \\in [1000,2000]. found e between x \\in [200,1500].\n", "we can see a near empty space around the x origin in both. lost seem to have less pt.\n", "\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[4.09e+04], [8.66e+03], [8.07e+04], ..., [5.63e+03], [6.29e+03], [2.26e+03]]\n", "[4.62e+04, 9.36e+03, 1.34e+05, 5.63e+04, ..., 2.01e+04, 6.94e+03, 7.83e+03]\n", "8657.132\n", "9355.866625028413\n" ] } ], "source": [ "energy_found = tracked[\"energy\"]\n", "energy_found = energy_found[tracked[\"brem_photons_pe_length\"]!=0]\n", "#ak.nan_to_num(energy_found)\n", "\n", "e_ph_found = tracked[\"brem_photons_pe\"]\n", "e_ph_found = e_ph_found[tracked[\"brem_photons_pe_length\"]!=0]\n", "#ak.nan_to_num(e_ph_found, nan=[0])\n", "e_ph_found = ak.sum(e_ph_found, axis=-1, keepdims=True)\n", "print(e_ph_found)\n", "print(energy_found)\n", "\n", "energy_lost = lost[\"energy\"]\n", "energy_lost = energy_lost[lost[\"brem_photons_pe_length\"]!=0]\n", "#ak.nan_to_num(energy_lost)\n", "\n", "e_ph_lost = lost[\"brem_photons_pe\"]\n", "e_ph_lost = e_ph_lost[lost[\"brem_photons_pe_length\"]!=0]\n", "#ak.nan_to_num(e_ph_lost)\n", "e_ph_lost = ak.sum(e_ph_lost, axis=-1,keepdims=True)\n", "\n", "#e_ph_found, energy_found = ak.broadcast_arrays(e_ph_found, energy_found)\n", "#e_ph_lost, energy_lost = ak.broadcast_arrays(e_ph_lost, energy_lost)\n", "\n", "e_ph_found = ak.to_numpy(ak.flatten(e_ph_found))\n", "energy_found = ak.to_numpy(energy_found)\n", "\n", "e_ph_lost = ak.to_numpy(ak.flatten(e_ph_lost))\n", "energy_lost = ak.to_numpy(energy_lost)\n", "\n", "print(e_ph_found[1])\n", "print(energy_found[1])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "q_e_found = e_ph_found/energy_found\n", "q_e_lost = e_ph_lost/energy_lost" ] }, { "cell_type": "code", "execution_count": 14, "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(q_e_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "plt.hist(q_e_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "plt.xlabel(r\"$E_\\gamma/E_0$\")\n", "plt.ylabel(\"counts (normed)\")\n", "plt.title(r'$E_{ph}/E_0$')\n", "plt.legend()\n", "\n", "\"\"\"\n", "B:\n", "we can clearly see that lost electrons are responsible for higher energy photons\n", "D:\n", "still able to see a trend that most electrons that give up all of their energy to photons are lost e. but nowhere near as extreme as for the B decay\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 2000x600 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0 = ax0.hist2d(e_ph_found/(1e3), energy_found/(1e3), density=False, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,50],[0,50]])\n", "ax0.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n", "ax0.set_ylabel(r\"$E_e$ [GeV]\")\n", "ax0.set_title(\"found electron energy against photon energy\")\n", "plt.colorbar(a0[3],ax=ax0)\n", "\n", "a1 = ax1.hist2d(e_ph_lost/(1e3), energy_lost/(1e3), density=False, bins=200, cmap=plt.cm.jet, cmin=1, range= [[0,50],[0,50]])\n", "ax1.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n", "ax1.set_ylabel(r\"$E_e$ [GeV]\")\n", "ax1.set_title(\"lost electron energy against photon energy\")\n", "plt.colorbar(a1[3],ax=ax1)\n", "\n", "\"\"\"\n", "B:\n", "concentrated at the E_ph/E_0~1 line especially at lower energies.\n", "lost E_ph to E_0: fewer entries at lower q_e\n", "D:\n", "both energies are much smaller than in the B decay. otherwise similar pattern.\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "brem_vtx_x_found = tracked[\"brem_vtx_x\"]\n", "brem_vtx_x_found = brem_vtx_x_found[tracked[\"brem_vtx_x_length\"]!=0]\n", "brem_vtx_x_found = ak.to_numpy(ak.flatten(brem_vtx_x_found))\n", "\n", "brem_vtx_z_found = tracked[\"brem_vtx_z\"]\n", "brem_vtx_z_found = brem_vtx_z_found[tracked[\"brem_vtx_z_length\"]!=0]\n", "#print(ak.to_numpy(brem_vtx_z_found))\n", "brem_vtx_z_found = ak.to_numpy(ak.flatten(brem_vtx_z_found))\n", "\n", "brem_vtx_x_lost = lost[\"brem_vtx_x\"]\n", "brem_vtx_x_lost = brem_vtx_x_lost[lost[\"brem_vtx_x_length\"]!=0]\n", "brem_vtx_x_lost = ak.to_numpy(ak.flatten(brem_vtx_x_lost))\n", "\n", "brem_vtx_z_lost = lost[\"brem_vtx_z\"]\n", "brem_vtx_z_lost = brem_vtx_z_lost[lost[\"brem_vtx_z_length\"]!=0]\n", "brem_vtx_z_lost = ak.to_numpy(ak.flatten(brem_vtx_z_lost))\n", "\n", "#vtx_x_fit= ak.to_numpy(vtx_x_found)\n", "#vtx_z_fit = ak.to_numpy(vtx_z_found)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0 = ax0.hist2d(brem_vtx_z_found, brem_vtx_x_found, density=False, bins=300, cmap=plt.cm.jet, cmin=1)\n", "ax0.set_xlabel(\"z [mm]\")\n", "ax0.set_ylabel(\"x [mm]\")\n", "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n", "\n", "plt.colorbar(a0[3],ax=ax0)\n", "\n", "a1 = ax1.hist2d(brem_vtx_z_lost, brem_vtx_x_lost, density=False, bins=300, cmap=plt.cm.jet, cmin=1)\n", "ax1.set_xlabel(\"z [mm]\")\n", "ax1.set_ylabel(\"x [mm]\")\n", "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n", "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", "\n", "plt.colorbar(a1[3], ax=ax1)\n", "\n", "\"\"\"\n", "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n", "B:\n", "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n", "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n", "D:\n", "lost brem vertices: we can very clearly see the concentration of vertices @ the beampipe\n", "both: less statistics in general, can still make out the tracking stations but not as well as in the B decay\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "#plot singular tracks by fitting brem vertices\n", "vtx_z_found = tracked[\"brem_vtx_z\"]\n", "vtx_z_found = vtx_z_found[tracked[\"brem_vtx_z_length\"]>3]\n", "\n", "vtx_x_found = tracked[\"brem_vtx_x\"]\n", "vtx_x_found = vtx_x_found[tracked[\"brem_vtx_x_length\"]>3]\n", "\n", "vtx_z_lost = lost[\"brem_vtx_z\"]\n", "vtx_z_lost = vtx_z_lost[lost[\"brem_vtx_z_length\"]>3]\n", "\n", "vtx_x_lost = lost[\"brem_vtx_x\"]\n", "vtx_x_lost = vtx_x_lost[lost[\"brem_vtx_x_length\"]>3]\n", "\n", "def cubic_fit(x, a, b, c, d):\n", " return (a + b*x + c*x**2 + d*x**3)\n", "\n", "def quint_fit(x, a, b, c, d, e, f):\n", " return (a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 2000x600 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "n_end=100\n", "\n", "for i in range(0,n_end):\n", " popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]))\n", " z_coord = np.linspace(vtx_z_found[i,0],12000,1000)\n", " fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n", " ax0.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n", " ax0.errorbar(ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]),fmt=\".\",ms=2)\n", "\n", "#ax0.legend()\n", "ax0.set_xlabel(\"z [mm]\")\n", "ax0.set_ylabel(\"x [mm]\")\n", "ax0.set_title(\"found tracks of brem vertices from few signals\")\n", "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n", "ax0.grid()\n", "\n", "for i in range(0,n_end):\n", " popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]))\n", " z_coord = np.linspace(vtx_z_lost[i,0],12000,1000)\n", " fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n", " ax1.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n", " ax1.errorbar(ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]),fmt=\".\",ms=2)\n", "\n", "#ax1.legend()\n", "ax1.set_xlabel(\"z [mm]\")\n", "ax1.set_ylabel(\"x [mm]\")\n", "ax1.set_title(\"lost tracks of brem vertices from few signals\")\n", "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n", "ax1.grid()\n", "\n", "\"\"\"\n", "B:\n", "we can see that of the lost brem vertices, many trajectory fits seem illogical and not plausible\n", "found: most seem like reasonable tracks\n", "D:\n", "both: many tracks arent good fits and are unusable\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "endvtx_x_found = tracked[\"all_endvtx_x\"]\n", "endvtx_x_found = endvtx_x_found[tracked[\"all_endvtx_x_length\"]!=0]\n", "endvtx_x_found = ak.to_numpy(ak.flatten(endvtx_x_found))\n", "\n", "endvtx_z_found = tracked[\"all_endvtx_z\"]\n", "endvtx_z_found = endvtx_z_found[tracked[\"all_endvtx_z_length\"]!=0]\n", "#print(ak.to_numpy(brem_vtx_z_found))\n", "endvtx_z_found = ak.to_numpy(ak.flatten(endvtx_z_found))\n", "\n", "endvtx_x_lost = lost[\"all_endvtx_x\"]\n", "endvtx_x_lost = endvtx_x_lost[lost[\"all_endvtx_x_length\"]!=0]\n", "endvtx_x_lost = ak.to_numpy(ak.flatten(endvtx_x_lost))\n", "\n", "endvtx_z_lost = lost[\"all_endvtx_z\"]\n", "endvtx_z_lost = endvtx_z_lost[lost[\"all_endvtx_z_length\"]!=0]\n", "endvtx_z_lost = ak.to_numpy(ak.flatten(endvtx_z_lost))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0 = ax0.hist2d(endvtx_z_found, endvtx_x_found, density=False, bins=500, cmap=plt.cm.jet, cmin=1)\n", "ax0.set_xlabel(\"z [mm]\")\n", "ax0.set_ylabel(\"x [mm]\")\n", "ax0.set_title(r\"$e^\\pm$ found end vertices\")\n", "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n", "\n", "plt.colorbar(a0[3],ax=ax0)\n", "\n", "a1 = ax1.hist2d(endvtx_z_lost, endvtx_x_lost, density=False, bins=500, cmap=plt.cm.jet, cmin=1)\n", "ax1.set_xlabel(\"z [mm]\")\n", "ax1.set_ylabel(\"x [mm]\")\n", "ax1.set_title(r\"$e^\\pm$ lost end vertices\")\n", "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n", "\n", "plt.colorbar(a1[3], ax=ax1)\n", "\n", "\"\"\"\n", "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n", "B:\n", "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n", "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n", "D:\n", "lost: densely located @ the beampipe.\n", "both: almost cant make out the velo or ut\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# try to plot trajectories using all tracker hits (Velo, UT, SciFi)\n", "\n", "velo_x_found = tracked[\"velo_hit_pos_x\"]\n", "velo_z_found = tracked[\"velo_hit_pos_z\"]\n", "ut_x_found = tracked[\"ut_hit_pos_x\"]\n", "ut_z_found = tracked[\"ut_hit_pos_z\"]\n", "scifi_x_found = tracked[\"scifi_hit_pos_x\"]\n", "scifi_z_found = tracked[\"scifi_hit_pos_z\"]\n", "\n", "tracker_x_found = ak.concatenate([velo_x_found,ut_x_found,scifi_x_found], axis=1)\n", "tracker_z_found = ak.concatenate([velo_z_found,ut_z_found,scifi_z_found], axis=1)\n", "\n", "velo_x_lost = lost[\"velo_hit_pos_x\"]\n", "velo_z_lost = lost[\"velo_hit_pos_z\"]\n", "ut_x_lost = lost[\"ut_hit_pos_x\"]\n", "ut_z_lost = lost[\"ut_hit_pos_z\"]\n", "scifi_x_lost = lost[\"scifi_hit_pos_x\"]\n", "scifi_z_lost = lost[\"scifi_hit_pos_z\"]\n", "\n", "tracker_x_lost = ak.concatenate([velo_x_lost,ut_x_lost,scifi_x_lost], axis=1)\n", "tracker_z_lost = ak.concatenate([velo_z_lost,ut_z_lost,scifi_z_lost], axis=1)\n", "\n", "\n", "#tracker_x_found = tracker_x_found[tracked[\"energy\"]>1e4]\n", "#tracker_z_found = tracker_z_found[tracked[\"energy\"]>1e4]\n", "\n", "#tracker_x_lost = tracker_x_lost[lost[\"energy\"]>1e4]\n", "#tracker_z_lost = tracker_z_lost[lost[\"energy\"]>1e4]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "nstart=0\n", "nend=130\n", "\n", "for i in range(nstart,nend):\n", " popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]))\n", " z_coord = np.linspace(tracker_z_found[i,0],14000,1000)\n", " fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n", " ax0.plot(z_coord, fit, \"-\", lw=0.5)\n", " ax0.errorbar(ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]),fmt=\".\",ms=3)\n", "\n", "ax0.legend([r\"$E>5$GeV\"])\n", "ax0.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n", "ax0.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n", "ax0.set_xticks(np.arange(0,14000,1000) , minor=True)\n", "ax0.set_yticks(np.arange(-4000,4000,500), minor=True)\n", "ax0.set_xlabel(\"z [mm]\")\n", "ax0.set_ylabel(\"x [mm]\")\n", "ax0.set_title(\"found tracks from detector hits from few signals\")\n", "ax0.set(xlim=(0,14000), ylim=(-4000,4000))\n", "ax0.grid()\n", "\n", "for i in range(nstart,nend):\n", " popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]))\n", " z_coord = np.linspace(tracker_z_lost[i,0],14000,1000)\n", " fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n", " ax1.plot(z_coord, fit, \"-\", lw=0.5)\n", " ax1.errorbar(ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]),fmt=\".\",ms=3)\n", "\n", "ax1.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n", "ax1.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n", "ax1.set_xticks(np.arange(0,14000,1000) , minor=True)\n", "ax1.set_yticks(np.arange(-4000,4000,500), minor=True)\n", "ax1.set_xlabel(\"z [mm]\")\n", "ax1.set_ylabel(\"x [mm]\")\n", "ax1.set_title(\"lost tracks from detector hits from few signals\")\n", "ax1.set(xlim=(0,14000), ylim=(-4000,4000))\n", "ax1.grid()\n", "\n", "\n", "\"\"\"\n", "electrons and photons will be stopped by the ECAL which serves to measure the particles energy\n", "\n", "B:\n", "the trajectories between the velo and tt should be linear, which cannot be plotted accurately using a single fit.\n", "lost tracks diverge more severely.\n", "\n", "most higher energy particles maintain a trajectory closer to the beamdirection ie a larger pseudorapidity,\n", "and show less bending in their trajectory, especially upstream.\n", "found: higher energy: very compact trajectory, less bending wrt lower energy particles \n", "\n", "D:\n", "E<10GeV: almost all diverge from the x origin (almost no hit for x<1500)\n", "E>10GeV: much more densely clustered. however still a noticeable empty space around the x origin\n", "\"\"\"\n", "\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "c = 299792458 #m/s\n", "energy_found = tracked[\"energy\"]\n", "p_found = tracked[\"p\"]\n", "pt_found = tracked[\"pt\"]\n", "eta_found = tracked[\"eta\"]\n", "\n", "energy_lost = lost[\"energy\"]\n", "p_lost = lost[\"p\"]\n", "pt_lost = lost[\"pt\"]\n", "eta_lost = lost[\"eta\"]\n", "\n", "p_found = ak.to_numpy(p_found)\n", "pt_found = ak.to_numpy(pt_found)\n", "eta_found = ak.to_numpy(eta_found)\n", "\n", "p_lost = ak.to_numpy(p_lost)\n", "pt_lost = ak.to_numpy(pt_lost)\n", "eta_lost = ak.to_numpy(eta_lost)\n", "#print(np.sqrt(energy_found[0]**2 - p_found[0]**2))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 2000x600 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(p_found, pt_found, bins=200, cmap=plt.cm.jet, cmin=0,range=[[0,1e4],[0,1e3]])\n", "ax0.set_xlabel(\"p\")\n", "ax0.set_ylabel(r\"$p_T$\")\n", "ax0.set_title(\"found electron momentum over transverse momentum\")\n", "plt.colorbar(a0[3],ax=ax0)\n", "\n", "a1=ax1.hist2d(p_lost, pt_lost, bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,1e4],[0,1e3]]) \n", "ax1.set_xlabel(\"p\")\n", "ax1.set_ylabel(r\"$p_T$\")\n", "ax1.set_title(\"lost electron momentum over transverse momentum\")\n", "plt.colorbar(a1[3],ax=ax1)\n", "\n", "\"\"\"\n", "B:\n", "\n", "D:\n", "both: clustered between 2000<p<6000 and 20<pt<400 (found a little more spread)\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 2000x600 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(eta_found, p_found/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,50]])\n", "ax0.set_xlabel(r\"$\\eta$\")\n", "ax0.set_ylabel(r\"$p$ [GeV]\")\n", "ax0.set_title(\"found eta and electron momentum\")\n", "plt.colorbar(a0[3],ax=ax0)\n", "\n", "a1=ax1.hist2d(eta_lost, p_lost/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,50]])\n", "ax1.set_xlabel(r\"$\\eta$\")\n", "ax1.set_ylabel(r\"$p$ [GeV]\")\n", "ax1.set_title(\"lost eta and electron momentum\")\n", "plt.colorbar(a1[3],ax=ax1)\n", "\n", "\"\"\"\n", "B:\n", "particles with lower momentum appear to have lower rapidity as well, ie a larger angle to the beam axis.\n", "D:\n", "both: clustered between 3<eta<5 and 0<p<10GeV. it seems that most particles had a higher rapidity \n", "\"\"\"\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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'all_endvtx_types_length': 11,\n", " 'all_endvtx_types': [101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 101.0,\n", " 0.0],\n", " 'all_endvtx_x_length': 11,\n", " 'all_endvtx_x': [19.496400833129883,\n", " 24.957000732421875,\n", " 32.490699768066406,\n", " 34.14419937133789,\n", " 34.6599006652832,\n", " 36.427101135253906,\n", " -1914.992431640625,\n", " -2413.033203125,\n", " -3782.947998046875,\n", " -3786.80810546875,\n", " -3819.826904296875],\n", " 'all_endvtx_y_length': 11,\n", " 'all_endvtx_y': [-20.370500564575195,\n", " -26.043100357055664,\n", " -33.85060119628906,\n", " -35.53160095214844,\n", " 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