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{ "cells": [ { "cell_type": "code", "execution_count": 16, "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": 17, "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": 18, "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": 19, "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(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": 20, "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": 21, "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": 22, "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": 23, "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": 24, "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)\n", "\n", "#brem_x_lost=np.array([])\n", "#brem_z_lost=np.array([])\n", "\n", "#for i in range(5):\n", "# brem_x_lost = np.append(brem_x_lost, brem_vtx_x_lost)\n", "\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "62968" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "ak.num(brem_vtx_z_found, axis=0)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "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[:11000], brem_vtx_x_found[:11000], density=False, bins=300, cmap=plt.cm.jet, cmin=1, vmax=30)\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, vmax=30)\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": 27, "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": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:1010: OptimizeWarning: Covariance of the parameters could not be estimated\n", " warnings.warn('Covariance of the parameters could not be estimated',\n" ] }, { "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": 29, "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": 30, "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": 31, "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": 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", "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": 33, "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": 34, "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": 35, "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": 36, "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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-20.351228713989258],\n", " 'velo_hit_pos_z_length': 10,\n", " 'velo_hit_pos_z': [100.64099884033203,\n", " 125.64099884033203,\n", " 150.64100646972656,\n", " 175.64100646972656,\n", " 200.64100646972656,\n", " 225.64100646972656,\n", " 250.64100646972656,\n", " 275.6409912109375,\n", " 324.3590087890625,\n", " 399.3590087890625],\n", " 'velo_track_idx': 143,\n", " 'velo_track_tx': 0.054571494460105896,\n", " 'velo_track_ty': -0.056447889655828476,\n", " 'velo_track_x': 39.710758209228516,\n", " 'velo_track_y': -41.2618293762207,\n", " 'velo_track_z': 770.0}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tracked[1].tolist()" ] }, { "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": 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