{ "cells": [ { "cell_type": "markdown", "id": "88c93f3a", "metadata": {}, "source": [ "## Jet Analysis(Nuclear Modification Factor)( $R_{\\mathrm{AA}}$)" ] }, { "cell_type": "markdown", "id": "b06e4546", "metadata": {}, "source": [ "# Preparations\n", "Please set path of jet data files for both pp and PbPb.\n", "Also if you use jet cone size different from $R=0.4$, please change the value jetR below." ] }, { "cell_type": "markdown", "id": "1dfb1c9a", "metadata": {}, "source": [ "# Loading all the files" ] }, { "cell_type": "code", "execution_count": 2, "id": "dcfa051a", "metadata": {}, "outputs": [], "source": [ "# Set File Paths\n", "# Please set the path for pp jet data file\n", "#|0.2|1->0.3|2->0.4|3->0.6|4->0.8|5->1.0|\n", "file_pp = '../../data/music_cms/cms_pp_vir0.5_R0.2_trkpT4.0_maxT250_pt200-1001_m1-7_merged_315769.dat'\n", "file_pp1 = '../../data/music_cms/cms_pp_vir0.5_R0.3_trkpT4.0_maxT250_pt200-1001_m1-7_merged_338726.dat'\n", "file_pp2 = '../../data/music_cms/cms_pp_vir0.5_R0.4_trkpT4.0_maxT250_pt200-1001_m1-7_merged_352683.dat'\n", "file_pp3 = '../../data/music_cms/cms_pp_vir0.5_R0.6_trkpT4.0_maxT250_pt200-1001_m1-7_merged_369925.dat'\n", "file_pp4 = '../../data/music_cms/cms_pp_vir0.5_R0.8_trkpT4.0_maxT250_pt200-1001_m1-7_merged_380962.dat'\n", "file_pp5 = '../../data/music_cms/cms_pp_vir0.5_R1.0_trkpT4.0_maxT250_pt200-1001_m1-7_merged_389140.dat'\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "d70a0423", "metadata": {}, "outputs": [], "source": [ "# Please set the path for pbpb jet data file(LBT ONLY)######\n", "#|0.2|1->0.3|2->0.4|3->0.6|4->0.8|5->1.0|\n", "file_pbpb = '../../data/music_cms/LBT/R0.2/cms_music_0-10_R0.2_s1-7_35k.dat'\n", "file_pbpb1 ='../../data/music_cms/LBT/R0.3/cms_music_0-10_R0.3_s1-7_35k.dat'\n", "file_pbpb2 ='../../data/music_cms/LBT/R0.4/cms_music_0-10_R0.4_s1-7_35k.dat'\n", "file_pbpb3 ='../../data/music_cms/LBT/R0.6/cms_music_0-10_R0.6_s1-7_35k.dat'\n", "file_pbpb4 ='../../data/music_cms/LBT/R0.8/cms_music_0-10_R0.8_s1-7_35k.dat'\n", "file_pbpb5 ='../../data/music_cms/LBT/R1.0/cms_music_0-10_R1.0_s1-7_35k.dat'\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "fcee61ca", "metadata": {}, "outputs": [], "source": [ "# preperations\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# pi and 2pi \n", "pi = 3.1415926\n", "twopi = 2.0*pi\n", "\n", "# define plot style\n", "width = 0.05\n", "plotMarkerSize = 8\n", "labelfontsize = 15\n", "import matplotlib as mpl\n", "mpl.rcParams['figure.figsize'] = [7.5, 6.5]\n", "mpl.rcParams['lines.linewidth'] = 2\n", "mpl.rcParams['xtick.top'] = True\n", "mpl.rcParams['xtick.labelsize'] = 15\n", "mpl.rcParams['xtick.major.width'] = 1.0\n", "mpl.rcParams['xtick.minor.width'] = 0.8\n", "mpl.rcParams['xtick.minor.visible'] = True\n", "mpl.rcParams['xtick.direction'] = \"in\"\n", "mpl.rcParams['ytick.right'] = True\n", "mpl.rcParams['ytick.labelsize'] = 15\n", "mpl.rcParams['ytick.major.width'] = 1.0\n", "mpl.rcParams['ytick.minor.width'] = 0.8\n", "mpl.rcParams['ytick.minor.visible'] = True\n", "mpl.rcParams['ytick.direction'] = \"in\"\n", "mpl.rcParams['legend.fontsize'] = 15\n", "mpl.rcParams['legend.numpoints'] = 1\n", "mpl.rcParams['font.size'] = 15\n", "mpl.rcParams['savefig.format'] = \"pdf\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "707d8005", "metadata": {}, "outputs": [], "source": [ "def ratio_error(v1,e1,v2,e2):\n", " #v1, e1: numerator value and error\n", " #v2, e2: denominator value and error \n", " error1 = e1/v2\n", " error2 = (e2/v2)*(v1/v2)\n", " error = np.sqrt(error1*error1+error2*error2)\n", " return error" ] }, { "cell_type": "code", "execution_count": 6, "id": "fe7826ff", "metadata": {}, "outputs": [], "source": [ "# Load files\n", "data_pp = np.loadtxt(file_pp, delimiter=',')\n", "data_pbpb = np.loadtxt(file_pbpb, delimiter=',')\n", "\n", "data_pp1 = np.loadtxt(file_pp1, delimiter=',')\n", "data_pbpb1 = np.loadtxt(file_pbpb1, delimiter=',')\n", "\n", "data_pp2 = np.loadtxt(file_pp2, delimiter=',')\n", "data_pbpb2 = np.loadtxt(file_pbpb2, delimiter=',')\n", "\n", "data_pp3 = np.loadtxt(file_pp3, delimiter=',')\n", "data_pbpb3 = np.loadtxt(file_pbpb3, delimiter=',')\n", "\n", "data_pp4 = np.loadtxt(file_pp4, delimiter=',')\n", "data_pbpb4 = np.loadtxt(file_pbpb4, delimiter=',')\n", "\n", "data_pp5 = np.loadtxt(file_pp5, delimiter=',')\n", "data_pbpb5 = np.loadtxt(file_pbpb5, delimiter=',')\n", "\n", "# Indices of the data array\n", "i_pp = data_pp[:,0] \n", "i_pbpb = data_pbpb[:,0] \n", "\n", "i_pp1 = data_pp1[:,0] \n", "i_pbpb1 = data_pbpb1[:,0]\n", "\n", "i_pp2 = data_pp2[:,0] \n", "i_pbpb2 = data_pbpb2[:,0]\n", "\n", "i_pp3 = data_pp3[:,0] \n", "i_pbpb3 = data_pbpb3[:,0]\n", "\n", "i_pp4 = data_pp4[:,0] \n", "i_pbpb4 = data_pbpb4[:,0]\n", "\n", "i_pp5 = data_pp5[:,0] \n", "i_pbpb5 = data_pbpb5[:,0]\n", "\n", "# Get Indices of jets in the data array\n", "jet_id_pp = np.where(i_pp < 0.1)\n", "jet_id_pbpb = np.where(i_pbpb < 0.1)\n", "\n", "jet_id_pp1 = np.where(i_pp1 < 0.1)\n", "jet_id_pbpb1 = np.where(i_pbpb1 < 0.1)\n", "\n", "jet_id_pp2 = np.where(i_pp2 < 0.1)\n", "jet_id_pbpb2 = np.where(i_pbpb2 < 0.1)\n", "\n", "jet_id_pp3 = np.where(i_pp3 < 0.1)\n", "jet_id_pbpb3 = np.where(i_pbpb3 < 0.1)\n", "\n", "jet_id_pp4 = np.where(i_pp4 < 0.1)\n", "jet_id_pbpb4 = np.where(i_pbpb4 < 0.1)\n", "\n", "jet_id_pp5 = np.where(i_pp5 < 0.1)\n", "jet_id_pbpb5 = np.where(i_pbpb5 < 0.1)\n", "\n", "# Extract jets\n", "jets_pp = data_pp[jet_id_pp]\n", "jets_pbpb = data_pbpb[jet_id_pbpb]\n", "\n", "jets_pp1 = data_pp1[jet_id_pp1]\n", "jets_pbpb1 = data_pbpb1[jet_id_pbpb1]\n", "\n", "jets_pp2 = data_pp2[jet_id_pp2]\n", "jets_pbpb2 = data_pbpb2[jet_id_pbpb2]\n", "\n", "jets_pp3 = data_pp3[jet_id_pp3]\n", "jets_pbpb3 = data_pbpb3[jet_id_pbpb3]\n", "\n", "jets_pp4 = data_pp4[jet_id_pp4]\n", "jets_pbpb4 = data_pbpb4[jet_id_pbpb4]\n", "\n", "jets_pp5 = data_pp5[jet_id_pp5]\n", "jets_pbpb5 = data_pbpb5[jet_id_pbpb5]\n", "\n", "# Extract associated charged particles for pp\n", "assoc_pp= []\n", "for i in range(len(jet_id_pp[0])-1):\n", " chunck = data_pp[jet_id_pp[0][i]+1:jet_id_pp[0][i+1]]\n", " assoc_pp.append(chunck)\n", "chunck = data_pp[jet_id_pp[0][-1]+1:]\n", "assoc_pp.append(chunck)\n", "\n", "assoc_pp1= []\n", "for i in range(len(jet_id_pp1[0])-1):\n", " chunck = data_pp1[jet_id_pp1[0][i]+1:jet_id_pp1[0][i+1]]\n", " assoc_pp1.append(chunck)\n", "chunck = data_pp1[jet_id_pp1[0][-1]+1:]\n", "assoc_pp1.append(chunck)\n", "\n", "assoc_pp2= []\n", "for i in range(len(jet_id_pp2[0])-1):\n", " chunck = data_pp2[jet_id_pp2[0][i]+1:jet_id_pp2[0][i+1]]\n", " assoc_pp2.append(chunck)\n", "chunck = data_pp2[jet_id_pp2[0][-1]+1:]\n", "assoc_pp2.append(chunck)\n", "\n", "assoc_pp3= []\n", "for i in range(len(jet_id_pp3[0])-1):\n", " chunck = data_pp3[jet_id_pp3[0][i]+1:jet_id_pp3[0][i+1]]\n", " assoc_pp3.append(chunck)\n", "chunck = data_pp3[jet_id_pp3[0][-1]+1:]\n", "assoc_pp3.append(chunck)\n", "\n", "assoc_pp4= []\n", "for i in range(len(jet_id_pp4[0])-1):\n", " chunck = data_pp4[jet_id_pp4[0][i]+1:jet_id_pp4[0][i+1]]\n", " assoc_pp4.append(chunck)\n", "chunck = data_pp4[jet_id_pp4[0][-1]+1:]\n", "assoc_pp4.append(chunck)\n", "\n", "assoc_pp5= []\n", "for i in range(len(jet_id_pp5[0])-1):\n", " chunck = data_pp5[jet_id_pp5[0][i]+1:jet_id_pp5[0][i+1]]\n", " assoc_pp5.append(chunck)\n", "chunck = data_pp5[jet_id_pp5[0][-1]+1:]\n", "assoc_pp5.append(chunck)\n", "\n", "# Extract associated charged particles for pbpb\n", "assoc_pbpb= []\n", "for i in range(len(jet_id_pbpb[0])-1):\n", " chunck = data_pbpb[jet_id_pbpb[0][i]+1:jet_id_pbpb[0][i+1]]\n", " assoc_pbpb.append(chunck)\n", "chunck = data_pbpb[jet_id_pbpb[0][-1]+1:]\n", "assoc_pbpb.append(chunck)\n", "\n", "assoc_pbpb1= []\n", "for i in range(len(jet_id_pbpb1[0])-1):\n", " chunck = data_pbpb1[jet_id_pbpb1[0][i]+1:jet_id_pbpb1[0][i+1]]\n", " assoc_pbpb1.append(chunck)\n", "chunck = data_pbpb1[jet_id_pbpb1[0][-1]+1:]\n", "assoc_pbpb1.append(chunck)\n", "\n", "assoc_pbpb2= []\n", "for i in range(len(jet_id_pbpb2[0])-1):\n", " chunck = data_pbpb2[jet_id_pbpb2[0][i]+1:jet_id_pbpb2[0][i+1]]\n", " assoc_pbpb2.append(chunck)\n", "chunck = data_pbpb2[jet_id_pbpb2[0][-1]+1:]\n", "assoc_pbpb2.append(chunck)\n", "\n", "assoc_pbpb3= []\n", "for i in range(len(jet_id_pbpb3[0])-1):\n", " chunck = data_pbpb3[jet_id_pbpb3[0][i]+1:jet_id_pbpb3[0][i+1]]\n", " assoc_pbpb3.append(chunck)\n", "chunck = data_pbpb3[jet_id_pbpb3[0][-1]+1:]\n", "assoc_pbpb3.append(chunck)\n", "\n", "assoc_pbpb4= []\n", "for i in range(len(jet_id_pbpb4[0])-1):\n", " chunck = data_pbpb4[jet_id_pbpb4[0][i]+1:jet_id_pbpb4[0][i+1]]\n", " assoc_pbpb4.append(chunck)\n", "chunck = data_pbpb4[jet_id_pbpb4[0][-1]+1:]\n", "assoc_pbpb4.append(chunck)\n", "\n", "assoc_pbpb5= []\n", "for i in range(len(jet_id_pbpb5[0])-1):\n", " chunck = data_pbpb5[jet_id_pbpb5[0][i]+1:jet_id_pbpb5[0][i+1]]\n", " assoc_pbpb5.append(chunck)\n", "chunck = data_pbpb5[jet_id_pbpb5[0][-1]+1:]\n", "assoc_pbpb5.append(chunck)" ] }, { "cell_type": "markdown", "id": "a38aafd6", "metadata": {}, "source": [ "## Jet-$R_{\\mathrm{AA}}$" ] }, { "cell_type": "code", "execution_count": 7, "id": "760626b2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R=0.3/0.2= [1.08107391 1.06643159 1.09826343 1.0993677 1.04522903]\n", "R=0.4/0.2= [1.13168569 1.10707858 1.15661189 1.1279165 1.09701365]\n", "R=0.6/0.2= [1.18848962 1.16407164 1.20533627 1.15082099 1.16323418]\n", "R=0.8/0.2= [1.2180547 1.19135318 1.22732049 1.16635575 1.1792821 ]\n", "R=1.0/0.2= [1.22978061 1.20244544 1.24267477 1.18146972 1.1779314 ]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Prepare arrays of Jet-pT\n", "jet_pt_pp_in = jets_pp[:,1]\n", "jet_pt_pbpb_in = jets_pbpb[:,1]\n", "\n", "jet_pt_pp1_in = jets_pp1[:,1]\n", "jet_pt_pbpb1_in = jets_pbpb1[:,1]\n", "\n", "jet_pt_pp2_in = jets_pp2[:,1]\n", "jet_pt_pbpb2_in = jets_pbpb2[:,1]\n", "\n", "jet_pt_pp3_in = jets_pp3[:,1]\n", "jet_pt_pbpb3_in = jets_pbpb3[:,1]\n", "\n", "jet_pt_pp4_in = jets_pp4[:,1]\n", "jet_pt_pbpb4_in = jets_pbpb4[:,1]\n", "\n", "jet_pt_pp5_in = jets_pp5[:,1]\n", "jet_pt_pbpb5_in = jets_pbpb5[:,1]\n", "\n", "\n", "bins_11 = [200,250,300,400,500,1000]\n", "pt_min = 200\n", "pt_max = 1000\n", "\n", "# Fill Histogram\n", "n_pp, pt = np.histogram(jet_pt_pp_in, bins= bins_11 )\n", "n_pbpb, pt = np.histogram(jet_pt_pbpb_in, bins= bins_11 )\n", "\n", "n_pp1, pt = np.histogram(jet_pt_pp1_in, bins= bins_11 )\n", "n_pbpb1, pt = np.histogram(jet_pt_pbpb1_in, bins= bins_11 )\n", "\n", "n_pp2, pt = np.histogram(jet_pt_pp2_in, bins= bins_11 )\n", "n_pbpb2, pt = np.histogram(jet_pt_pbpb2_in, bins= bins_11 )\n", "\n", "n_pp3, pt = np.histogram(jet_pt_pp3_in, bins= bins_11 )\n", "n_pbpb3, pt = np.histogram(jet_pt_pbpb3_in, bins= bins_11 )\n", "\n", "n_pp4, pt = np.histogram(jet_pt_pp4_in, bins= bins_11 )\n", "n_pbpb4, pt = np.histogram(jet_pt_pbpb4_in, bins= bins_11 )\n", "\n", "n_pp5, pt = np.histogram(jet_pt_pp5_in, bins= bins_11 )\n", "n_pbpb5, pt = np.histogram(jet_pt_pbpb5_in, bins= bins_11 )\n", "\n", "# Statistical Errors\n", "err_n_pp = np.sqrt(n_pp)\n", "err_n_pbpb = np.sqrt(n_pbpb)\n", "\n", "err_n_pp1 = np.sqrt(n_pp1)\n", "err_n_pbpb1 = np.sqrt(n_pbpb1)\n", "\n", "err_n_pp2 = np.sqrt(n_pp2)\n", "err_n_pbpb2 = np.sqrt(n_pbpb2)\n", "\n", "err_n_pp3 = np.sqrt(n_pp3)\n", "err_n_pbpb3 = np.sqrt(n_pbpb3)\n", "\n", "err_n_pp4 = np.sqrt(n_pp4)\n", "err_n_pbpb4 = np.sqrt(n_pbpb4)\n", "\n", "err_n_pp5 = np.sqrt(n_pp5)\n", "err_n_pbpb5 = np.sqrt(n_pbpb5)\n", "\n", "# bin width\n", "dpt = (pt[1:]-pt[:-1])\n", "# bin center\n", "pt = pt[0:-1] + 0.5*dpt\n", "\n", "\n", "n_ev_pp = 315769\n", "n_ev_pp1 = 338726 \n", "n_ev_pp2 = 352683 \n", "n_ev_pp3 = 369925\n", "n_ev_pp4 = 380962 \n", "n_ev_pp5 = 389140\n", "\n", "\n", "n_ev_pbpb = 35000\n", "sigma = 5.15\n", "d_eta = 2*2\n", "\n", "# Jet Spectrum\n", "dn_dpt_pp = (n_pp*sigma)/n_ev_pp/dpt/d_eta\n", "dn_dpt_pbpb = (n_pbpb*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "dn_dpt_pp1 = (n_pp1*sigma)/n_ev_pp1/dpt/d_eta\n", "dn_dpt_pbpb1 = (n_pbpb1*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "dn_dpt_pp2 = (n_pp2*sigma)/n_ev_pp2/dpt/d_eta\n", "dn_dpt_pbpb2 = (n_pbpb2*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "dn_dpt_pp3 = (n_pp3*sigma)/n_ev_pp3/dpt/d_eta\n", "dn_dpt_pbpb3 = (n_pbpb3*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "dn_dpt_pp4 = (n_pp4*sigma)/n_ev_pp4/dpt/d_eta\n", "dn_dpt_pbpb4 = (n_pbpb4*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "dn_dpt_pp5 = (n_pp5*sigma)/n_ev_pp5/dpt/d_eta\n", "dn_dpt_pbpb5 = (n_pbpb5*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "# Errors \n", "err_dn_dpt_pp = (err_n_pp*sigma)/n_ev_pp/dpt/d_eta\n", "err_dn_dpt_pbpb = (err_n_pbpb*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "err_dn_dpt_pp1 = (err_n_pp1*sigma)/n_ev_pp1/dpt/d_eta\n", "err_dn_dpt_pbpb1 = (err_n_pbpb1*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "err_dn_dpt_pp2 = (err_n_pp2*sigma)/n_ev_pp2/dpt/d_eta\n", "err_dn_dpt_pbpb2 = (err_n_pbpb2*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "err_dn_dpt_pp3 = (err_n_pp3*sigma)/n_ev_pp3/dpt/d_eta\n", "err_dn_dpt_pbpb3 = (err_n_pbpb3*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "err_dn_dpt_pp4 = (err_n_pp4*sigma)/n_ev_pp4/dpt/d_eta\n", "err_dn_dpt_pbpb4 = (err_n_pbpb4*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "err_dn_dpt_pp5 = (err_n_pp5*sigma)/n_ev_pp5/dpt/d_eta\n", "err_dn_dpt_pbpb5 = (err_n_pbpb5*sigma)/n_ev_pbpb/dpt/d_eta\n", "\n", "# Generate Plots\n", "fig = plt.figure()\n", "\n", "# Calculate RAA and error\n", "raa = dn_dpt_pbpb/dn_dpt_pp\n", "error_raa = ratio_error(dn_dpt_pbpb,err_dn_dpt_pbpb,dn_dpt_pp,err_dn_dpt_pp)\n", "\n", "raa1 = dn_dpt_pbpb1/dn_dpt_pp1\n", "error_raa1 = ratio_error(dn_dpt_pbpb1,err_dn_dpt_pbpb1,dn_dpt_pp1,err_dn_dpt_pp1)\n", "\n", "raa2 = dn_dpt_pbpb2/dn_dpt_pp2\n", "error_raa2 = ratio_error(dn_dpt_pbpb2,err_dn_dpt_pbpb2,dn_dpt_pp2,err_dn_dpt_pp2)\n", "\n", "raa3 = dn_dpt_pbpb3/dn_dpt_pp3\n", "error_raa3 = ratio_error(dn_dpt_pbpb3,err_dn_dpt_pbpb3,dn_dpt_pp3,err_dn_dpt_pp3)\n", "\n", "raa4 = dn_dpt_pbpb4/dn_dpt_pp4\n", "error_raa4 = ratio_error(dn_dpt_pbpb4,err_dn_dpt_pbpb4,dn_dpt_pp4,err_dn_dpt_pp4)\n", "\n", "raa5 = dn_dpt_pbpb5/dn_dpt_pp5\n", "error_raa5 = ratio_error(dn_dpt_pbpb5,err_dn_dpt_pbpb5,dn_dpt_pp5,err_dn_dpt_pp5)\n", "\n", "err_1 = ratio_error(raa1,error_raa1,raa,error_raa)\n", "\n", "err_2 = ratio_error(raa2,error_raa2,raa,error_raa)\n", "\n", "err_3 = ratio_error(raa3,error_raa3,raa,error_raa)\n", "\n", "err_4 = ratio_error(raa4,error_raa4,raa,error_raa)\n", "\n", "err_5 = ratio_error(raa5,error_raa5,raa,error_raa)\n", "\n", "\n", "##########\n", "\n", "plt.errorbar(pt, raa2/raa, ms=9,mfc='red',mec='black',fmt='o', label=r\"R=0.4\",\n", " xerr=0.5*dpt, yerr=err_2, color='red')\n", "\n", "plt.errorbar(pt, raa3/raa, ms=9,mfc='blue',mec='black',fmt='o', label=r\"R=0.6\",\n", " xerr=0.5*dpt, yerr=err_3, color='blue')\n", "\n", "plt.errorbar(pt, raa4/raa, ms=9,mfc='green',mec='black',fmt='o', label=r\"R=0.8\",\n", " xerr=0.5*dpt, yerr=err_4, color='green')\n", "\n", "plt.errorbar(pt, raa5/raa, ms=9,mfc='cyan',mec='black',fmt='o', label=r\"R=1.0\",\n", " xerr=0.5*dpt, yerr=err_5, color='cyan')\n", "\n", "\n", "\n", "#axes setting\n", "plt.legend(loc='upper left')\n", "plt.xscale('log')\n", "plt.xlabel(r\"$p^{\\mathrm{jet}}_{\\mathrm{T}}$ [GeV]\")\n", "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n", "plt.xlim(pt_min,pt_max)\n", "plt.ylim(0.8,2.0)\n", "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5) \n", "#plt.text(pt_min+5,-0.05, r\"PbPb(0-10%)\"r\"$\\sqrt{s_{\\mathrm{NN}}}=5.02 TeV$\")\n", "#plt.text(pt_min+5,-0.15,r'$anti-k_{\\mathrm{t}},R=0.4,(|y_{\\mathrm{jet}}|<2)$')\n", "#plt.text(pt_min+5,-0.25,r\"$JS(MATTER+LBT),Q_{\\mathrm{sw}}=2GeV$\")\n", "\n", "# save plot to the Desktop\n", "plt.tight_layout()\n", "plt.savefig('cms_double_pT_0-10')\n", "print(\"R=0.3/0.2=\", raa1/raa)\n", "print(\"R=0.4/0.2=\",raa2/raa)\n", "print(\"R=0.6/0.2=\",raa3/raa)\n", "print(\"R=0.8/0.2=\",raa4/raa)\n", "print(\"R=1.0/0.2=\",raa5/raa)" ] }, { "cell_type": "code", "execution_count": 8, "id": "a64d3017", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "R03 = raa1/raa\n", "R04 = raa2/raa\n", "R06 = raa3/raa\n", "R08 = raa4/raa\n", "R10 = raa5/raa\n", "\n", "\n", "#print(R03)\n", "#print(R04)\n", "#print(R06)\n", "#print(R08)\n", "#print(R10)\n", "\n", "R_bins = [0.3,0.4,0.6,0.8,1.0]\n", "\n", "\n", "R_CMS_300_400 = [1.019,0,0,0,0]\n", "R_CMS_400_500 = [1.038,1.048,1.103,1.12,0]\n", "R_CMS_500_1000 = [0.998,0.933,0.948,0.987,1.02]\n", "#R_CMS_500_1000_err = [0.01,0.01,0.01,0.01,0.01]\n", "\n", "R_200_250 = [R03[0], R04[0], R06[0], R08[0], R10[0]]\n", "R_250_300 = [R03[1], R04[1], R06[1], R08[1], R10[1]]\n", "R_300_400 = [R03[2], R04[2], R06[2], R08[2], R10[2]]\n", "R_400_500 = [R03[3], R04[3], R06[3], R08[3], R10[3]]\n", "R_500_1000 = [R03[4], R04[4], R06[4], R08[4], R10[4]]\n", "\n", "R_500_1000_M = [0.9*R03[4], 0.9*R04[4], 0.9*R06[4], 0.9*R08[4], 0.9*R10[4]]\n", "R_500_1000_A = [0.85*R03[4], 0.85*R04[4], 0.85*R06[4], 0.85*R08[4], 0.85*R10[4]]\n", "\n", "\n", "#plt.errorbar(R_bins, R_CMS_300_400,ms=8,mfc='black',mec='black', fmt='s', label=\"CMS_300_400\", xerr=None, yerr=None, color='black')\n", "#plt.errorbar(R_bins, R_CMS_400_500,ms=8,mfc='black',mec='black', fmt='s', label=\"CMS_400_500\", xerr=None, yerr=None, color='black')\n", "plt.errorbar(R_bins, R_CMS_500_1000,ms=8,mfc='black',mec='black', fmt='s', label=\"CMS_500_1000\", xerr=None, yerr=None, color='black')\n", "\n", "\n", "#plt.errorbar(R_bins, R_200_250,ms=8,mfc='yellow',mec='black', fmt='s', label=\"200-250 M+LBT\",xerr=None, yerr=None, color='black')\n", "#plt.errorbar(R_bins, R_250_300,ms=8,mfc='blue',mec='black', fmt='s', label=\"250-300 M+LBT\",xerr=None, yerr=None, color='black')\n", "#plt.errorbar(R_bins, R_300_400,ms=8,mfc='green',mec='black', fmt='s', label=\"300-400 M+LBT\",xerr=None, yerr=None, color='black')\n", "#plt.errorbar(R_bins, R_400_500,ms=8,mfc='red',mec='black', fmt='s', label=\"400-500 M+LBT\",xerr=None, yerr=None, color='black')\n", "plt.errorbar(R_bins, R_500_1000,ms=8,mfc='magenta',mec='black', fmt='s', label=\"500-1000 M+LBT\",xerr=None, yerr=None, color='black')\n", "plt.errorbar(R_bins, R_500_1000_M,ms=8,mfc='Red',mec='black', fmt='s', label=\"500-1000 M+Martini\",xerr=None, yerr=None, color='black')\n", "plt.errorbar(R_bins, R_500_1000_A,ms=8,mfc='Blue',mec='black', fmt='s', label=\"500-1000 M+ADS\",xerr=None, yerr=None, color='black')\n", "\n", "\n", "#plt.xscale('log')\n", "\n", "plt.legend(loc='upper left')\n", "plt.ylim(0.7,2.0)\n", "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5) \n", "\n", "\n", "fig = plt.figure()" ] }, { "cell_type": "markdown", "id": "fc25de68", "metadata": {}, "source": [ "# CMS-jetR-300-400" ] }, { "cell_type": "markdown", "id": "7a61d37f", "metadata": {}, "source": [ "# for martini and ads load this file with reco respectively, and get the values of R_x00_y00 (3 arrays) and input that array in R_x00_y00_M and R_x00_y00_A to plot all three models simultaneously." ] }, { "cell_type": "code", "execution_count": 42, "id": "d3f26e06", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mpl.rcParams['figure.figsize'] = [7, 6]\n", "\n", "\n", "R_CMS_300_400 = [1.019,0,0,0,0]\n", "err_300_400 = [0.047,0,0,0,0]\n", "\n", "R_300_400 = [R03[2], R04[2], R06[2], R08[2], R10[2]]\n", "err_JS_300_400 = [err_1[2],err_2[2],err_3[2],err_4[2],err_5[2]]\n", "\n", "#input the arrays obtained from loading martini and ads individually\n", "R_300_400_M = [1.087421457060924, 1.1371201834649416, 1.1923907263828304, 1.2017355836199048, 1.2122445381874856]\n", "err_JS_300_400_M = [0.05722454528682157, 0.058917362987257485, 0.06069459864960222, 0.060693234914417186, 0.06080044276875342]\n", "\n", "R_300_400_A = [1.097410140890613, 1.1605204479420221, 1.1891569592333242, 1.2159964562826286, 1.2255819350077892]\n", "err_JS_300_400_A = [0.06931080402058196, 0.07199129946631468, 0.07284418231214698, 0.07368295058305094, 0.07376735984984974]\n", "\n", "\n", "plt.errorbar(R_bins, R_CMS_300_400,ms=8,mfc='black',mec='black', fmt='s', label=\"CMS(0-10%)\", xerr=None, yerr=err_300_400, color='black')\n", "\n", "plt.errorbar(R_bins, R_300_400,ms=7,mfc='magenta',mec='black', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_300_400, color='magenta')\n", "\n", "plt.errorbar(R_bins, R_300_400_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_300_400_M, color='red')\n", "\n", "plt.errorbar(R_bins, R_300_400_A,ms=7,mfc='Blue',mec='black', fmt='s', label=\"MATTER+AdS/CFT\",xerr=None, yerr=err_JS_300_400_A, color='blue')\n", "\n", "\n", "plt.legend(loc='upper left')\n", "plt.xlabel(r\"$jet-R$\")\n", "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n", "plt.ylim(0.7,1.6)\n", "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n", "plt.text(0.7,1.5,r\"$300" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "R_CMS_400_500 = [1.038,1.048,1.103,1.12,0]\n", "err_400_500 =[0.051,0.057,0.098,0.161,0]\n", "\n", "R_400_500 = [R03[3], R04[3], R06[3], R08[3], R10[3]]\n", "err_JS_400_500 = [err_1[3],err_2[3],err_3[3],err_4[3],err_5[3]]\n", "\n", "#input the arrays obtained from loading martini and ads individually\n", "R_400_500_M = [1.107902948996912, 1.1417333072053066, 1.1667852266008045, 1.2077353007835328, 1.2923555587444864]\n", "err_JS_400_500_M = [0.15511130554633093, 0.15791887747022149, 0.15901250213085974, 0.16216272340527998, 0.17006620500934602]\n", "\n", "R_400_500_A = [1.0834254707157707, 1.1832065256294306, 1.2180126541687064, 1.280160791901948, 1.2604416948018737]\n", "err_JS_400_500_A = [0.16368694118847074, 0.17431058714420328, 0.1765823604999301, 0.18235590293651, 0.17894588462428027]\n", "\n", "plt.errorbar(R_bins, R_CMS_400_500,ms=8,mfc='black',mec='black', fmt='s', label=\"CMS(0-10%)\", xerr=None, yerr=err_400_500, color='black')\n", "\n", "plt.errorbar(R_bins, R_400_500,ms=7,mfc='magenta',mec='black', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_400_500, color='magenta')\n", "\n", "plt.errorbar(R_bins, R_400_500_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_400_500_M, color='red')\n", "\n", "plt.errorbar(R_bins, R_400_500_A,ms=7,mfc='Blue',mec='black', fmt='s', label=\"MATTER+AdS/CFT\",xerr=None, yerr=err_JS_400_500_A, color='blue')\n", "\n", "\n", "plt.legend(loc='upper left')\n", "plt.xlabel(r\"$jet-R$\")\n", "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n", "plt.ylim(0.7,1.6)\n", "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n", "plt.text(0.7,1.5,r\"$400" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "R_CMS_500_1000 = [0.998,0.933,0.948,0.987,1.02]\n", "err_500_1000 = [0.065,0.077,0.096,0.113,0.148]\n", "\n", "R_500_1000 = [R03[4], R04[4], R06[4], R08[4], R10[4]]\n", "err_JS_500_1000 = [err_1[4],err_2[4],err_3[4],err_4[4],err_5[4]]\n", "\n", "#input the arrays obtained from loading martini and ads individually\n", "R_500_1000_M = [1.0985570406473855, 1.1472664622184126, 1.3028718885511639, 1.3537250450673393, 1.296983750176387]\n", "err_JS_500_1000_M = [0.25694207865218277, 0.26393293540568064, 0.2890884673570552, 0.295898961361187, 0.2834177949359136]\n", "\n", "R_500_1000_A = [1.0894023986419905, 1.1205858468179841, 1.2517788733138633, 1.4357689871926322, 1.485635932020225]\n", "err_JS_500_1000_A = [0.38765515035748915, 0.3935150661543935, 0.42512527616212037, 0.47116679185456184, 0.48071687800133]\n", "\n", "#####\n", "\n", "plt.errorbar(R_bins, R_CMS_500_1000,ms=8,mfc='black',mec='black', fmt='s', label=\"CMS(0-10%)\", xerr=None, yerr=err_500_1000, color='black')\n", "\n", "plt.errorbar(R_bins, R_500_1000,ms=7,mfc='magenta',mec='blue', fmt='s', label=\"MATTER+LBT\",xerr=None, yerr=err_JS_500_1000, color='magenta')\n", "\n", "plt.errorbar(R_bins, R_500_1000_M,ms=7,mfc='Red',mec='black', fmt='s', label=\"MATTER+MARTINI\",xerr=None, yerr=err_JS_500_1000_M, color='red')\n", "\n", "plt.errorbar(R_bins, R_500_1000_A,ms=7,mfc='Blue',mec='black', fmt='s', label=\"MATTER+AdS/CFT\",xerr=None, yerr=err_JS_500_1000_A, color='blue')\n", "\n", "\n", "plt.legend(loc='upper left')\n", "plt.xlabel(r\"$jet-R$\")\n", "plt.ylabel(r\"$R^{\\mathrm{R}}_{\\mathrm{AA}}/R^{\\mathrm{R=0.2}}_{\\mathrm{AA}}$\")\n", "plt.ylim(0.7,1.8)\n", "plt.axhline(1, color = \"blue\", linestyle=\"dashed\", linewidth=1.5)\n", "plt.text(0.7,1.7,r\"$500