{
"cells": [
{
"cell_type": "markdown",
"id": "a8b8add6",
"metadata": {},
"source": [
"# df103_NanoAODHiggsAnalysis\n",
"An example of complex analysis with RDataFrame: reconstructing the Higgs boson.\n",
"\n",
"This tutorial is a simplified but yet complex example of an analysis reconstructing the Higgs boson decaying to two Z\n",
"bosons from events with four leptons. The data and simulated events are taken from CERN OpenData representing a\n",
"subset of the data recorded in 2012 with the CMS detector at the LHC. The tutorials follows the Higgs to four leptons\n",
"analysis published on CERN Open Data portal ([10.7483/OPENDATA.CMS.JKB8.RR42](http://opendata.cern.ch/record/5500)).\n",
"The resulting plots show the invariant mass of the selected four lepton systems in different decay modes (four muons,\n",
"four electrons and two of each kind) and in a combined plot indicating the decay of the Higgs boson with a mass of\n",
"about 125 GeV.\n",
"\n",
"The following steps are performed for each sample with data and simulated events in order to reconstruct the Higgs\n",
"boson from the selected muons and electrons:\n",
"1. Select interesting events with multiple cuts on event properties, e.g., number of leptons, kinematics of the\n",
" leptons and quality of the tracks.\n",
"2. Reconstruct two Z bosons of which only one on the mass shell from the selected events and apply additional cuts on\n",
" the reconstructed objects.\n",
"3. Reconstruct the Higgs boson from the remaining Z boson candidates and calculate its invariant mass.\n",
"\n",
"Another aim of this version of the tutorial is to show a way to blend C++ and Python code. All the functions that\n",
"make computations on data to define new columns or filter existing ones in a precise way, better suited to be written\n",
"in C++, have been moved to a header that is then declared to the ROOT C++ interpreter. The functions that instead\n",
"create nodes of the computational graph (e.g. Filter, Define) remain inside the main Python script.\n",
"\n",
"The tutorial has the fast mode enabled by default, which reads the data from already skimmed\n",
"datasets with a total size of only 51MB. If the fast mode is disabled, the tutorial runs over\n",
"the full dataset with a size of 12GB.\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Stefan Wunsch (KIT, CERN), Vincenzo Eduardo Padulano (UniMiB, CERN) \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 19, 2026 at 08:10 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8fc13f2c",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:32.206055Z",
"iopub.status.busy": "2026-05-19T20:10:32.205955Z",
"iopub.status.idle": "2026-05-19T20:10:33.232049Z",
"shell.execute_reply": "2026-05-19T20:10:33.231450Z"
}
},
"outputs": [],
"source": [
"import ROOT\n",
"import os"
]
},
{
"cell_type": "markdown",
"id": "85b03382",
"metadata": {},
"source": [
"Enable multi-threading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "98ededbc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:33.233379Z",
"iopub.status.busy": "2026-05-19T20:10:33.233249Z",
"iopub.status.idle": "2026-05-19T20:10:33.366138Z",
"shell.execute_reply": "2026-05-19T20:10:33.365584Z"
}
},
"outputs": [],
"source": [
"ROOT.ROOT.EnableImplicitMT()"
]
},
{
"cell_type": "markdown",
"id": "c9d96191",
"metadata": {},
"source": [
"Include necessary header"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "690c9ffd",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:33.368124Z",
"iopub.status.busy": "2026-05-19T20:10:33.368000Z",
"iopub.status.idle": "2026-05-19T20:10:33.567539Z",
"shell.execute_reply": "2026-05-19T20:10:33.566991Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"higgs_header_path = os.path.join(os.sep, str(ROOT.gROOT.GetTutorialDir()) + os.sep, \"analysis\" + os.sep, \n",
" \"dataframe\" + os.sep, \"df103_NanoAODHiggsAnalysis_python.h\")\n",
"\n",
"ROOT.gInterpreter.Declare('#include \"{}\"'.format(higgs_header_path))"
]
},
{
"cell_type": "markdown",
"id": "72b16946",
"metadata": {},
"source": [
"Python functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d7153a32",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:33.569079Z",
"iopub.status.busy": "2026-05-19T20:10:33.568958Z",
"iopub.status.idle": "2026-05-19T20:10:33.678164Z",
"shell.execute_reply": "2026-05-19T20:10:33.677593Z"
}
},
"outputs": [],
"source": [
"def reco_higgs_to_2el2mu(df):\n",
" \"\"\"Reconstruct Higgs from two electrons and two muons\"\"\"\n",
" # Filter interesting events\n",
" df_base = selection_2el2mu(df)\n",
" # Compute masses of Z systems\n",
" df_z_mass = df_base.Define(\"Z_mass\", \"compute_z_masses_2el2mu(Electron_pt, Electron_eta, Electron_phi,\"\n",
" \" Electron_mass, Muon_pt, Muon_eta, Muon_phi, Muon_mass)\")\n",
" # Cut on mass of Z candidates\n",
" df_z_cut = filter_z_candidates(df_z_mass)\n",
" # Reconstruct H mass\n",
" df_h_mass = df_z_cut.Define(\"H_mass\", \"compute_higgs_mass_2el2mu(Electron_pt, Electron_eta, Electron_phi,\"\n",
" \" Electron_mass, Muon_pt, Muon_eta, Muon_phi, Muon_mass)\")\n",
"\n",
" return df_h_mass\n",
"\n",
"\n",
"def selection_2el2mu(df):\n",
" \"\"\"Select interesting events with two electrons and two muons\"\"\"\n",
" df_ge2el2mu = df.Filter(\"nElectron>=2 && nMuon>=2\", \"At least two electrons and two muons\")\n",
" df_eta = df_ge2el2mu.Filter(\"All(abs(Electron_eta)<2.5) && All(abs(Muon_eta)<2.4)\", \"Eta cuts\")\n",
"\n",
" df_pt = df_eta.Filter(\"pt_cuts(Muon_pt, Electron_pt)\", \"Pt cuts\")\n",
"\n",
" df_dr = df_pt.Filter(\"dr_cuts(Muon_eta, Muon_phi, Electron_eta, Electron_phi)\", \"Dr cuts\")\n",
"\n",
" df_iso = df_dr.Filter(\"All(abs(Electron_pfRelIso03_all)<0.40) && All(abs(Muon_pfRelIso04_all)<0.40)\",\n",
" \"Require good isolation\")\n",
" df_el_ip3d = df_iso.Define(\"Electron_ip3d_el\", \"sqrt(Electron_dxy*Electron_dxy + Electron_dz*Electron_dz)\")\n",
" df_el_sip3d = df_el_ip3d.Define(\"Electron_sip3d_el\",\n",
" \"Electron_ip3d_el/sqrt(Electron_dxyErr*Electron_dxyErr + \"\n",
" \"Electron_dzErr*Electron_dzErr)\")\n",
" df_el_track = df_el_sip3d.Filter(\"All(Electron_sip3d_el<4) && All(abs(Electron_dxy)<0.5) &&\"\n",
" \" All(abs(Electron_dz)<1.0)\",\n",
" \"Electron track close to primary vertex with small uncertainty\")\n",
" df_mu_ip3d = df_el_track.Define(\"Muon_ip3d_mu\", \"sqrt(Muon_dxy*Muon_dxy + Muon_dz*Muon_dz)\")\n",
"\n",
" df_mu_sip3d = df_mu_ip3d.Define(\"Muon_sip3d_mu\",\n",
" \"Muon_ip3d_mu/sqrt(Muon_dxyErr*Muon_dxyErr + Muon_dzErr*Muon_dzErr)\")\n",
" df_mu_track = df_mu_sip3d.Filter(\"All(Muon_sip3d_mu<4) && All(abs(Muon_dxy)<0.5) && All(abs(Muon_dz)<1.0)\",\n",
" \"Muon track close to primary vertex with small uncertainty\")\n",
" df_2p2n = df_mu_track.Filter(\"Sum(Electron_charge)==0 && Sum(Muon_charge)==0\",\n",
" \"Two opposite charged electron and muon pairs\")\n",
"\n",
" return df_2p2n\n",
"\n",
"\n",
"def reco_higgs_to_4mu(df):\n",
" \"\"\"Reconstruct Higgs from four muons\"\"\"\n",
" # Filter interesting events\n",
" df_base = selection_4mu(df)\n",
"\n",
" # Reconstruct Z systems\n",
" df_z_idx = df_base.Define(\"Z_idx\", \"reco_zz_to_4l(Muon_pt, Muon_eta, Muon_phi, Muon_mass, Muon_charge)\")\n",
"\n",
" # Cut on distance between muons building Z systems\n",
" df_z_dr = df_z_idx.Filter(\"filter_z_dr(Z_idx, Muon_eta, Muon_phi)\", \"Delta R separation of muons building Z system\")\n",
"\n",
" # Compute masses of Z systems\n",
" df_z_mass = df_z_dr.Define(\"Z_mass\", \"compute_z_masses_4l(Z_idx, Muon_pt, Muon_eta, Muon_phi, Muon_mass)\")\n",
"\n",
" # Cut on mass of Z candidates\n",
" df_z_cut = filter_z_candidates(df_z_mass)\n",
"\n",
" # Reconstruct H mass\n",
" df_h_mass = df_z_cut.Define(\"H_mass\", \"compute_higgs_mass_4l(Z_idx, Muon_pt, Muon_eta, Muon_phi, Muon_mass)\")\n",
"\n",
" return df_h_mass\n",
"\n",
"\n",
"def selection_4mu(df):\n",
" \"\"\"Select interesting events with four muons\"\"\"\n",
" df_ge4m = df.Filter(\"nMuon>=4\", \"At least four muons\")\n",
"\n",
" df_iso = df_ge4m.Filter(\"All(abs(Muon_pfRelIso04_all)<0.40)\", \"Require good isolation\")\n",
" df_kin = df_iso.Filter(\"All(Muon_pt>5) && All(abs(Muon_eta)<2.4)\", \"Good muon kinematics\")\n",
" df_ip3d = df_kin.Define(\"Muon_ip3d\", \"sqrt(Muon_dxy*Muon_dxy + Muon_dz*Muon_dz)\")\n",
" df_sip3d = df_ip3d.Define(\"Muon_sip3d\", \"Muon_ip3d/sqrt(Muon_dxyErr*Muon_dxyErr + Muon_dzErr*Muon_dzErr)\")\n",
" df_pv = df_sip3d.Filter(\"All(Muon_sip3d<4) && All(abs(Muon_dxy)<0.5) && All(abs(Muon_dz)<1.0)\",\n",
" \"Track close to primary vertex with small uncertainty\")\n",
" df_2p2n = df_pv.Filter(\"nMuon==4 && Sum(Muon_charge==1)==2 && Sum(Muon_charge==-1)==2\",\n",
" \"Two positive and two negative muons\")\n",
"\n",
" return df_2p2n\n",
"\n",
"\n",
"def filter_z_candidates(df):\n",
" \"\"\"Apply selection on reconstructed Z candidates\"\"\"\n",
" df_z1_cut = df.Filter(\"Z_mass[0] > 40 && Z_mass[0] < 120\", \"Mass of first Z candidate in [40, 120]\")\n",
" df_z2_cut = df_z1_cut.Filter(\"Z_mass[1] > 12 && Z_mass[1] < 120\", \"Mass of second Z candidate in [12, 120]\")\n",
"\n",
" return df_z2_cut\n",
"\n",
"\n",
"def reco_higgs_to_4el(df):\n",
" \"\"\"Reconstruct Higgs from four electrons\"\"\"\n",
" # Filter interesting events\n",
" df_base = selection_4el(df)\n",
"\n",
" # Reconstruct Z systems\n",
" df_z_idx = df_base.Define(\"Z_idx\",\n",
" \"reco_zz_to_4l(Electron_pt, Electron_eta, Electron_phi, Electron_mass, Electron_charge)\")\n",
"\n",
" # Cut on distance between Electrons building Z systems\n",
" df_z_dr = df_z_idx.Filter(\"filter_z_dr(Z_idx, Electron_eta, Electron_phi)\",\n",
" \"Delta R separation of Electrons building Z system\")\n",
"\n",
" # Compute masses of Z systems\n",
" df_z_mass = df_z_dr.Define(\"Z_mass\",\n",
" \"compute_z_masses_4l(Z_idx, Electron_pt, Electron_eta, Electron_phi, Electron_mass)\")\n",
"\n",
" # Cut on mass of Z candidates\n",
" df_z_cut = filter_z_candidates(df_z_mass)\n",
"\n",
" # Reconstruct H mass\n",
" df_h_mass = df_z_cut.Define(\"H_mass\",\n",
" \"compute_higgs_mass_4l(Z_idx, Electron_pt, Electron_eta, Electron_phi, Electron_mass)\")\n",
"\n",
" return df_h_mass\n",
"\n",
"\n",
"def selection_4el(df):\n",
" \"\"\"Select interesting events with four electrons\"\"\"\n",
" df_ge4el = df.Filter(\"nElectron>=4\", \"At least our electrons\")\n",
" df_iso = df_ge4el.Filter(\"All(abs(Electron_pfRelIso03_all)<0.40)\", \"Require good isolation\")\n",
" df_kin = df_iso.Filter(\"All(Electron_pt>7) && All(abs(Electron_eta)<2.5)\", \"Good Electron kinematics\")\n",
" df_ip3d = df_kin.Define(\"Electron_ip3d\", \"sqrt(Electron_dxy*Electron_dxy + Electron_dz*Electron_dz)\")\n",
" df_sip3d = df_ip3d.Define(\"Electron_sip3d\",\n",
" \"Electron_ip3d/sqrt(Electron_dxyErr*Electron_dxyErr + Electron_dzErr*Electron_dzErr)\")\n",
" df_pv = df_sip3d.Filter(\"All(Electron_sip3d<4) && All(abs(Electron_dxy)<0.5) && All(abs(Electron_dz)<1.0)\",\n",
" \"Track close to primary vertex with small uncertainty\")\n",
" df_2p2n = df_pv.Filter(\"nElectron==4 && Sum(Electron_charge==1)==2 && Sum(Electron_charge==-1)==2\",\n",
" \"Two positive and two negative electrons\")\n",
"\n",
" return df_2p2n\n",
"\n",
"\n",
"def plot(sig, bkg, data, x_label, filename):\n",
" \"\"\"\n",
" Plot invariant mass for signal and background processes from simulated\n",
" events overlay the measured data.\n",
" \"\"\"\n",
" # Canvas and general style options\n",
" ROOT.gStyle.SetTextFont(42)\n",
" d = ROOT.TCanvas(\"\", \"\", 800, 700)\n",
" d.SetLeftMargin(0.15)\n",
"\n",
" # Get signal and background histograms and stack them to show Higgs signal\n",
" # on top of the background process\n",
" h_bkg = bkg.Clone()\n",
" h_cmb = sig.Clone()\n",
"\n",
" h_cmb.Add(h_bkg)\n",
" h_cmb.SetTitle(\"\")\n",
" h_cmb.GetXaxis().SetTitle(x_label)\n",
" h_cmb.GetXaxis().SetTitleSize(0.04)\n",
" h_cmb.GetYaxis().SetTitle(\"N_{Events}\")\n",
" h_cmb.GetYaxis().SetTitleSize(0.04)\n",
" h_cmb.SetLineColor(\"kRed\")\n",
" h_cmb.SetLineWidth(2)\n",
" h_cmb.SetMaximum(18)\n",
" h_cmb.SetStats(False)\n",
"\n",
" h_bkg.SetLineWidth(2)\n",
" h_bkg.SetFillStyle(1001)\n",
" h_bkg.SetLineColor(\"black\")\n",
" h_bkg.SetFillColor(\"kAzure-9\")\n",
"\n",
" # Get histogram of data points\n",
" h_data = data.Clone()\n",
" h_data.SetLineWidth(1)\n",
" h_data.SetMarkerStyle(20)\n",
" h_data.SetMarkerSize(1.0)\n",
" h_data.SetMarkerColor(\"black\")\n",
" h_data.SetLineColor(\"black\")\n",
"\n",
" # Draw histograms\n",
" h_cmb.Draw(\"HIST\")\n",
" h_bkg.Draw(\"HIST SAME\")\n",
" h_data.Draw(\"PE1 SAME\")\n",
"\n",
" # Add legend\n",
" legend = ROOT.TLegend(0.62, 0.70, 0.82, 0.88)\n",
" legend.SetFillColor(0)\n",
" legend.SetBorderSize(0)\n",
" legend.SetTextSize(0.03)\n",
" legend.AddEntry(h_data, \"Data\", \"pe\")\n",
" legend.AddEntry(h_bkg, \"ZZ\", \"f\")\n",
" legend.AddEntry(h_cmb, \"m_{H} = 125 GeV\", \"f\")\n",
" legend.Draw()\n",
"\n",
" # Add header\n",
" cms_label = ROOT.TLatex()\n",
" cms_label.SetTextSize(0.04)\n",
" cms_label.DrawLatexNDC(0.16, 0.92, \"#bf{CMS Open Data}\")\n",
" header = ROOT.TLatex()\n",
" header.SetTextSize(0.03)\n",
" header.DrawLatexNDC(0.63, 0.92, \"#sqrt{s} = 8 TeV, L_{int} = 11.6 fb^{-1}\")\n",
"\n",
" # Save plot\n",
" d.SaveAs(filename)\n",
"\n",
" # Make sure canvas and objects remains existing after the macro execution\n",
" ROOT.SetOwnership(d, False)\n",
" ROOT.SetOwnership(h_cmb, False)\n",
" ROOT.SetOwnership(h_data, False)\n",
" ROOT.SetOwnership(h_bkg, False)\n",
" ROOT.SetOwnership(legend, False)"
]
},
{
"cell_type": "markdown",
"id": "e54d8d12",
"metadata": {},
"source": [
"In fast mode, take samples from */cms_opendata_2012_nanoaod_skimmed/*, which has\n",
"the preselections from the selection_* functions already applied."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8b461de3",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:33.679500Z",
"iopub.status.busy": "2026-05-19T20:10:33.679377Z",
"iopub.status.idle": "2026-05-19T20:10:33.786186Z",
"shell.execute_reply": "2026-05-19T20:10:33.785619Z"
}
},
"outputs": [],
"source": [
"path = \"root://eospublic.cern.ch//eos/root-eos/cms_opendata_2012_nanoaod/\"\n",
"run_fast = True # Run on skimmed data, set to False to run on full dataset\n",
"if run_fast: path = \"root://eospublic.cern.ch//eos/root-eos/cms_opendata_2012_nanoaod_skimmed/\""
]
},
{
"cell_type": "markdown",
"id": "a74eaf68",
"metadata": {},
"source": [
"Create dataframes for signal, background and data samples"
]
},
{
"cell_type": "markdown",
"id": "66a1a0c5",
"metadata": {},
"source": [
"Signal: Higgs -> 4 leptons"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "800f9f3f",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:33.787686Z",
"iopub.status.busy": "2026-05-19T20:10:33.787536Z",
"iopub.status.idle": "2026-05-19T20:10:34.518084Z",
"shell.execute_reply": "2026-05-19T20:10:34.517492Z"
}
},
"outputs": [],
"source": [
"df_sig_4l = ROOT.RDataFrame(\"Events\", path + \"SMHiggsToZZTo4L.root\")"
]
},
{
"cell_type": "markdown",
"id": "811111e4",
"metadata": {},
"source": [
"Background: ZZ -> 4 leptons\n",
"Note that additional background processes from the original paper\n",
"with minor contribution were left out for this\n",
"tutorial."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "531dfd73",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:34.520311Z",
"iopub.status.busy": "2026-05-19T20:10:34.520185Z",
"iopub.status.idle": "2026-05-19T20:10:34.812531Z",
"shell.execute_reply": "2026-05-19T20:10:34.810694Z"
}
},
"outputs": [],
"source": [
"df_bkg_4mu = ROOT.RDataFrame(\"Events\", path + \"ZZTo4mu.root\")\n",
"df_bkg_4el = ROOT.RDataFrame(\"Events\", path + \"ZZTo4e.root\")\n",
"df_bkg_2el2mu = ROOT.RDataFrame(\"Events\", path + \"ZZTo2e2mu.root\")"
]
},
{
"cell_type": "markdown",
"id": "e133e088",
"metadata": {},
"source": [
"CMS data taken in 2012 (11.6 fb^-1 integrated luminosity)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ab14e285",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:34.821181Z",
"iopub.status.busy": "2026-05-19T20:10:34.821042Z",
"iopub.status.idle": "2026-05-19T20:10:35.262946Z",
"shell.execute_reply": "2026-05-19T20:10:35.242518Z"
}
},
"outputs": [],
"source": [
"df_data_doublemu = ROOT.RDataFrame(\"Events\", (path + f for f in [\"Run2012B_DoubleMuParked.root\", \"Run2012C_DoubleMuParked.root\"]))\n",
"df_data_doubleel = ROOT.RDataFrame(\"Events\", (path + f for f in [\"Run2012B_DoubleElectron.root\", \"Run2012C_DoubleElectron.root\"]))"
]
},
{
"cell_type": "markdown",
"id": "931bf4ee",
"metadata": {},
"source": [
"Number of bins for all histograms"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ab7e2270",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:35.271286Z",
"iopub.status.busy": "2026-05-19T20:10:35.271103Z",
"iopub.status.idle": "2026-05-19T20:10:35.425772Z",
"shell.execute_reply": "2026-05-19T20:10:35.414723Z"
}
},
"outputs": [],
"source": [
"nbins = 36"
]
},
{
"cell_type": "markdown",
"id": "8f7a2eca",
"metadata": {},
"source": [
"Weights"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7b9eee34",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:35.438818Z",
"iopub.status.busy": "2026-05-19T20:10:35.438641Z",
"iopub.status.idle": "2026-05-19T20:10:35.594714Z",
"shell.execute_reply": "2026-05-19T20:10:35.578721Z"
}
},
"outputs": [],
"source": [
"luminosity = 11580.0 # Integrated luminosity of the data samples\n",
"\n",
"xsec_ZZTo4mu = 0.077 # ZZ->4mu: Standard Model cross-section\n",
"nevt_ZZTo4mu = 1499064.0 # ZZ->4mu: Number of simulated events\n",
"\n",
"xsec_ZZTo4el = 0.077 # ZZ->4el: Standard Model cross-section\n",
"nevt_ZZTo4el = 1499093.0 # ZZ->4el: Number of simulated events\n",
"\n",
"xsec_ZZTo2el2mu = 0.18 # ZZ->2el2mu: Standard Model cross-section\n",
"nevt_ZZTo2el2mu = 1497445.0 # ZZ->2el2mu: Number of simulated events\n",
"\n",
"xsec_SMHiggsToZZTo4L = 0.0065 # H->4l: Standard Model cross-section\n",
"nevt_SMHiggsToZZTo4L = 299973.0 # H->4l: Number of simulated events\n",
"scale_ZZTo4l = 1.386 # ZZ->4l: Scale factor for ZZ to four leptons\n",
"\n",
"weight_sig_4mu = luminosity * xsec_SMHiggsToZZTo4L / nevt_SMHiggsToZZTo4L\n",
"weight_bkg_4mu = luminosity * xsec_ZZTo4mu * scale_ZZTo4l / nevt_ZZTo4mu\n",
"\n",
"weight_sig_4el = luminosity * xsec_SMHiggsToZZTo4L / nevt_SMHiggsToZZTo4L\n",
"weight_bkg_4el = luminosity * xsec_ZZTo4el * scale_ZZTo4l / nevt_ZZTo4el\n",
"\n",
"weight_sig_2el2mu = luminosity * xsec_SMHiggsToZZTo4L / nevt_SMHiggsToZZTo4L\n",
"weight_bkg_2el2mu = luminosity * xsec_ZZTo2el2mu * scale_ZZTo4l / nevt_ZZTo2el2mu"
]
},
{
"cell_type": "markdown",
"id": "d69ab9f6",
"metadata": {},
"source": [
"Reconstruct Higgs to 4 muons"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "68ad8bbe",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:35.600199Z",
"iopub.status.busy": "2026-05-19T20:10:35.600043Z",
"iopub.status.idle": "2026-05-19T20:10:36.217065Z",
"shell.execute_reply": "2026-05-19T20:10:36.216599Z"
}
},
"outputs": [],
"source": [
"df_sig_4mu_reco = reco_higgs_to_4mu(df_sig_4l)\n",
"\n",
"df_h_sig_4mu = df_sig_4mu_reco.Define(\"weight\", \"{}\".format(weight_sig_4mu))\\\n",
" .Histo1D((\"h_sig_4mu\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")\n",
"\n",
"df_bkg_4mu_reco = reco_higgs_to_4mu(df_bkg_4mu)\n",
"\n",
"df_h_bkg_4mu = df_bkg_4mu_reco.Define(\"weight\", \"{}\".format(weight_bkg_4mu))\\\n",
" .Histo1D((\"h_bkg_4mu\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")\n",
"\n",
"df_data_4mu_reco = reco_higgs_to_4mu(df_data_doublemu)\n",
"\n",
"df_h_data_4mu = df_data_4mu_reco.Define(\"weight\", \"1.0\")\\\n",
" .Histo1D((\"h_data_4mu\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")"
]
},
{
"cell_type": "markdown",
"id": "f39c886f",
"metadata": {},
"source": [
"Reconstruct Higgs to 4 electrons"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "64423799",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:36.224754Z",
"iopub.status.busy": "2026-05-19T20:10:36.224584Z",
"iopub.status.idle": "2026-05-19T20:10:36.349126Z",
"shell.execute_reply": "2026-05-19T20:10:36.348699Z"
}
},
"outputs": [],
"source": [
"df_sig_4el_reco = reco_higgs_to_4el(df_sig_4l)\n",
"\n",
"df_h_sig_4el = df_sig_4el_reco.Define(\"weight\", \"{}\".format(weight_sig_4el))\\\n",
" .Histo1D((\"h_sig_4el\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")\n",
"\n",
"df_bkg_4el_reco = reco_higgs_to_4el(df_bkg_4el)\n",
"\n",
"df_h_bkg_4el = df_bkg_4el_reco.Define(\"weight\", \"{}\".format(weight_bkg_4el))\\\n",
" .Histo1D((\"h_bkg_4el\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")\n",
"\n",
"df_data_4el_reco = reco_higgs_to_4el(df_data_doubleel)\n",
"\n",
"df_h_data_4el = df_data_4el_reco.Define(\"weight\", \"1.0\")\\\n",
" .Histo1D((\"h_data_4el\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")"
]
},
{
"cell_type": "markdown",
"id": "de855406",
"metadata": {},
"source": [
"Reconstruct Higgs to 2 electrons and 2 muons"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2a356495",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:36.371144Z",
"iopub.status.busy": "2026-05-19T20:10:36.370969Z",
"iopub.status.idle": "2026-05-19T20:10:36.574090Z",
"shell.execute_reply": "2026-05-19T20:10:36.573623Z"
}
},
"outputs": [],
"source": [
"df_sig_2el2mu_reco = reco_higgs_to_2el2mu(df_sig_4l)\n",
"\n",
"df_h_sig_2el2mu = df_sig_2el2mu_reco.Define(\"weight\", \"{}\".format(weight_sig_2el2mu))\\\n",
" .Histo1D((\"h_sig_2el2mu\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")\n",
"\n",
"df_bkg_2el2mu_reco = reco_higgs_to_2el2mu(df_bkg_2el2mu)\n",
"\n",
"df_h_bkg_2el2mu = df_bkg_2el2mu_reco.Define(\"weight\", \"{}\".format(weight_bkg_2el2mu))\\\n",
" .Histo1D((\"h_bkg_2el2mu\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")\n",
"\n",
"df_data_2el2mu_reco = reco_higgs_to_2el2mu(df_data_doublemu)\n",
"\n",
"df_h_data_2el2mu = df_data_2el2mu_reco.Define(\"weight\", \"1.0\")\\\n",
" .Histo1D((\"h_data_2el2mu_doublemu\", \"\", nbins, 70, 180), \"H_mass\", \"weight\")"
]
},
{
"cell_type": "markdown",
"id": "d8c00243",
"metadata": {},
"source": [
"RunGraphs allows to run the event loops of the separate RDataFrame graphs\n",
"concurrently. This results in an improved usage of the available resources\n",
"if each separate RDataFrame can not utilize all available resources, e.g.,\n",
"because not enough data is available."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "35c9b191",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:36.588050Z",
"iopub.status.busy": "2026-05-19T20:10:36.587908Z",
"iopub.status.idle": "2026-05-19T20:10:41.099698Z",
"shell.execute_reply": "2026-05-19T20:10:41.098512Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ROOT.RDF.RunGraphs([df_h_sig_4mu, df_h_bkg_4mu, df_h_data_4mu,\n",
" df_h_sig_4el, df_h_bkg_4el, df_h_data_4el,\n",
" df_h_sig_2el2mu, df_h_bkg_2el2mu, df_h_data_2el2mu])"
]
},
{
"cell_type": "markdown",
"id": "d1836160",
"metadata": {},
"source": [
"Get histograms (does not rerun the event loop)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9e25a18c",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:41.119263Z",
"iopub.status.busy": "2026-05-19T20:10:41.119098Z",
"iopub.status.idle": "2026-05-19T20:10:41.266746Z",
"shell.execute_reply": "2026-05-19T20:10:41.256003Z"
}
},
"outputs": [],
"source": [
"signal_4mu = df_h_sig_4mu.GetValue()\n",
"background_4mu = df_h_bkg_4mu.GetValue()\n",
"data_4mu = df_h_data_4mu.GetValue()\n",
"\n",
"signal_4el = df_h_sig_4el.GetValue()\n",
"background_4el = df_h_bkg_4el.GetValue()\n",
"data_4el = df_h_data_4el.GetValue()\n",
"\n",
"signal_2el2mu = df_h_sig_2el2mu.GetValue()\n",
"background_2el2mu = df_h_bkg_2el2mu.GetValue()\n",
"data_2el2mu = df_h_data_2el2mu.GetValue()"
]
},
{
"cell_type": "markdown",
"id": "4ddc7b97",
"metadata": {},
"source": [
"Make plots"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "de48d585",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:41.272426Z",
"iopub.status.busy": "2026-05-19T20:10:41.272267Z",
"iopub.status.idle": "2026-05-19T20:10:41.657731Z",
"shell.execute_reply": "2026-05-19T20:10:41.657152Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : pdf file higgs_4mu.pdf has been created\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : pdf file higgs_4el.pdf has been created\n",
"Info in : pdf file higgs_2el2mu.pdf has been created\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(signal_4mu, background_4mu, data_4mu, \"m_{4#mu} (GeV)\", \"higgs_4mu.pdf\")\n",
"plot(signal_4el, background_4el, data_4el, \"m_{4e} (GeV)\", \"higgs_4el.pdf\")\n",
"plot(signal_2el2mu, background_2el2mu, data_2el2mu, \"m_{2e2#mu} (GeV)\", \"higgs_2el2mu.pdf\")"
]
},
{
"cell_type": "markdown",
"id": "c8a215b4",
"metadata": {},
"source": [
"Combined plots\n",
"If this was done before plotting the others, calling the `Add` function\n",
"on the `signal_4mu` histogram would modify the underlying `TH1D` object.\n",
"Thus, the histogram with the 4 muons reconstruction would be lost,\n",
"instead resulting in the same plot as the aggregated histograms."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "176c51ef",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:41.673355Z",
"iopub.status.busy": "2026-05-19T20:10:41.673202Z",
"iopub.status.idle": "2026-05-19T20:10:41.777293Z",
"shell.execute_reply": "2026-05-19T20:10:41.776634Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h_sig_4l = signal_4mu\n",
"h_sig_4l.Add(signal_4el)\n",
"h_sig_4l.Add(signal_2el2mu)\n",
"\n",
"h_bkg_4l = background_4mu\n",
"h_bkg_4l.Add(background_4el)\n",
"h_bkg_4l.Add(background_2el2mu)\n",
"\n",
"h_data_4l = data_4mu\n",
"h_data_4l.Add(data_4el)\n",
"h_data_4l.Add(data_2el2mu)"
]
},
{
"cell_type": "markdown",
"id": "c4077f24",
"metadata": {},
"source": [
"Plot aggregated histograms"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "e755b9b6",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:41.782320Z",
"iopub.status.busy": "2026-05-19T20:10:41.782172Z",
"iopub.status.idle": "2026-05-19T20:10:41.955820Z",
"shell.execute_reply": "2026-05-19T20:10:41.945151Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : pdf file higgs_4l.pdf has been created\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(h_sig_4l, h_bkg_4l, h_data_4l, \"m_{4l} (GeV)\", \"higgs_4l.pdf\")"
]
},
{
"cell_type": "markdown",
"id": "03ba3f28",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ce36faf7",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:10:41.988653Z",
"iopub.status.busy": "2026-05-19T20:10:41.988454Z",
"iopub.status.idle": "2026-05-19T20:10:42.173835Z",
"shell.execute_reply": "2026-05-19T20:10:42.173248Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}