df106_HiggsToFourLeptons.py

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## \file
## \ingroup tutorial_dataframe
## \notebook -draw
## The Higgs to four lepton analysis from the ATLAS Open Data release of 2020, with RDataFrame.
##
## This tutorial is the Higgs to four lepton analysis from the ATLAS Open Data release in 2020
## (http://opendata.atlas.cern/release/2020/documentation/). The data was taken with the ATLAS detector
## during 2016 at a center-of-mass energy of 13 TeV. The decay of the Standard Model Higgs boson
## to two Z bosons and subsequently to four leptons is called the "golden channel". The selection leads
## to a narrow invariant mass peak on top a relatively smooth and small background, revealing
## the Higgs at 125 GeV.
##
## The analysis is translated to a RDataFrame workflow processing about 300 MB of simulated events and data.
##
## \macro_image
## \macro_code
## \macro_output
##
## \date March 2020
## \author Stefan Wunsch (KIT, CERN)
 
import ROOT
import json
import os
 
# Create a ROOT dataframe for each dataset
# Note that we load the filenames from the external json file placed in the same folder than this script.
path = "root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22"
files = json.load(open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "df106_HiggsToFourLeptons.json")))
processes = files.keys()
df = {}
xsecs = {}
sumws = {}
samples = []
for p in processes:
    for d in files[p]:
        # Construct the dataframes
        folder = d[0] # Folder name
        sample = d[1] # Sample name
        xsecs[sample] = d[2] # Cross-section
        sumws[sample] = d[3] # Sum of weights
        samples.append(sample)
        df[sample] = ROOT.RDataFrame("mini", "{}/4lep/{}/{}.4lep.root".format(path, folder, sample))
 
# Select events for the analysis
ROOT.gInterpreter.Declare("""
using cRVecF = const ROOT::RVecF &;
bool GoodElectronsAndMuons(const ROOT::RVecI & type, cRVecF pt, cRVecF eta, cRVecF phi, cRVecF e, cRVecF trackd0pv, cRVecF tracksigd0pv, cRVecF z0)
{
    for (size_t i = 0; i < type.size(); i++) {
        ROOT::Math::PtEtaPhiEVector p(pt[i] / 1000.0, eta[i], phi[i], e[i] / 1000.0);
        if (type[i] == 11) {
            if (pt[i] < 7000 || abs(eta[i]) > 2.47 || abs(trackd0pv[i] / tracksigd0pv[i]) > 5 || abs(z0[i] * sin(p.Theta())) > 0.5) return false;
        } else {
            if (abs(trackd0pv[i] / tracksigd0pv[i]) > 5 || abs(z0[i] * sin(p.Theta())) > 0.5) return false;
        }
    }
    return true;
}
""")
 
for s in samples:
    # Select electron or muon trigger
    df[s] = df[s].Filter("trigE || trigM")
 
    # Select events with exactly four good leptons conserving charge and lepton numbers
    # Note that all collections are RVecs and good_lep is the mask for the good leptons.
    # The lepton types are PDG numbers and set to 11 or 13 for an electron or muon
    # irrespective of the charge.
    df[s] = df[s].Define("good_lep", "abs(lep_eta) < 2.5 && lep_pt > 5000 && lep_ptcone30 / lep_pt < 0.3 && lep_etcone20 / lep_pt < 0.3")\
                 .Filter("Sum(good_lep) == 4")\
                 .Filter("Sum(lep_charge[good_lep]) == 0")\
                 .Define("goodlep_sumtypes", "Sum(lep_type[good_lep])")\
                 .Filter("goodlep_sumtypes == 44 || goodlep_sumtypes == 52 || goodlep_sumtypes == 48")
 
    # Apply additional cuts depending on lepton flavour
    df[s] = df[s].Filter("GoodElectronsAndMuons(lep_type[good_lep], lep_pt[good_lep], lep_eta[good_lep], lep_phi[good_lep], lep_E[good_lep], lep_trackd0pvunbiased[good_lep], lep_tracksigd0pvunbiased[good_lep], lep_z0[good_lep])")
 
    # Create new columns with the kinematics of good leptons
    df[s] = df[s].Define("goodlep_pt", "lep_pt[good_lep]")\
                 .Define("goodlep_eta", "lep_eta[good_lep]")\
                 .Define("goodlep_phi", "lep_phi[good_lep]")\
                 .Define("goodlep_E", "lep_E[good_lep]")
 
    # Select leptons with high transverse momentum
    df[s] = df[s].Filter("goodlep_pt[0] > 25000 && goodlep_pt[1] > 15000 && goodlep_pt[2] > 10000")
 
# Apply luminosity, scale factors and MC weights for simulated events
lumi = 10064.0
for s in samples:
    if "data" in s:
        df[s] = df[s].Define("weight", "1.0")
    else:
        df[s] = df[s].Define("weight", "scaleFactor_ELE * scaleFactor_MUON * scaleFactor_LepTRIGGER * scaleFactor_PILEUP * mcWeight * {} / {} * {}".format(xsecs[s], sumws[s], lumi))
 
# Compute invariant mass of the four lepton system and make a histogram
ROOT.gInterpreter.Declare("""
float ComputeInvariantMass(cRVecF pt, cRVecF eta, cRVecF phi, cRVecF e)
{
    ROOT::Math::PtEtaPhiEVector p1(pt[0], eta[0], phi[0], e[0]);
    ROOT::Math::PtEtaPhiEVector p2(pt[1], eta[1], phi[1], e[1]);
    ROOT::Math::PtEtaPhiEVector p3(pt[2], eta[2], phi[2], e[2]);
    ROOT::Math::PtEtaPhiEVector p4(pt[3], eta[3], phi[3], e[3]);
    return (p1 + p2 + p3 + p4).M() / 1000;
}
""")
 
histos = {}
for s in samples:
    df[s] = df[s].Define("m4l", "ComputeInvariantMass(goodlep_pt, goodlep_eta, goodlep_phi, goodlep_E)")
    histos[s] = df[s].Histo1D(ROOT.RDF.TH1DModel(s, "m4l", 24, 80, 170), "m4l", "weight")
 
# Run the event loop and merge histograms of the respective processes
 
# RunGraphs allows to run the event loops of the separate RDataFrame graphs
# concurrently. This results in an improved usage of the available resources
# if each separate RDataFrame can not utilize all available resources, e.g.,
# because not enough data is available.
ROOT.RDF.RunGraphs([histos[s] for s in samples])
 
def merge_histos(label):
    h = None
    for i, d in enumerate(files[label]):
        t = histos[d[1]].GetValue()
        if i == 0: h = t.Clone()
        else: h.Add(t)
    h.SetNameTitle(label, label)
    return h
 
data = merge_histos("data")
higgs = merge_histos("higgs")
zz = merge_histos("zz")
other = merge_histos("other")
 
# Apply MC correction for ZZ due to missing gg->ZZ process
zz.Scale(1.3)
 
# Create the plot
 
# Set styles
ROOT.gROOT.SetStyle("ATLAS")
 
# Create canvas with pad
c = ROOT.TCanvas("c", "", 600, 600)
pad = ROOT.TPad("upper_pad", "", 0, 0, 1, 1)
pad.SetTickx(False)
pad.SetTicky(False)
pad.Draw()
pad.cd()
 
# Draw stack with MC contributions
stack = ROOT.THStack()
for h, color in zip([other, zz, higgs], [(155, 152, 204), (100, 192, 232), (191, 34, 41)]):
    h.SetLineWidth(1)
    h.SetLineColor(1)
    h.SetFillColor(ROOT.TColor.GetColor(*color))
    stack.Add(h)
stack.Draw("HIST")
stack.GetXaxis().SetLabelSize(0.04)
stack.GetXaxis().SetTitleSize(0.045)
stack.GetXaxis().SetTitleOffset(1.3)
stack.GetXaxis().SetTitle("m_{4l}^{H#rightarrow ZZ} [GeV]")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetLabelSize(0.04)
stack.GetYaxis().SetTitleSize(0.045)
stack.SetMaximum(33)
stack.GetYaxis().ChangeLabel(1, -1, 0)
 
# Draw data
data.SetMarkerStyle(20)
data.SetMarkerSize(1.2)
data.SetLineWidth(2)
data.SetLineColor(ROOT.kBlack)
data.Draw("E SAME")
 
# Add legend
legend = ROOT.TLegend(0.60, 0.65, 0.92, 0.92)
legend.SetTextFont(42)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.04)
legend.SetTextAlign(32)
legend.AddEntry(data, "Data" ,"lep")
legend.AddEntry(higgs, "Higgs", "f")
legend.AddEntry(zz, "ZZ", "f")
legend.AddEntry(other, "Other", "f")
legend.Draw("SAME")
 
# Add ATLAS label
text = ROOT.TLatex()
text.SetNDC()
text.SetTextFont(72)
text.SetTextSize(0.045)
text.DrawLatex(0.21, 0.86, "ATLAS")
text.SetTextFont(42)
text.DrawLatex(0.21 + 0.16, 0.86, "Open Data")
text.SetTextSize(0.04)
text.DrawLatex(0.21, 0.80, "#sqrt{s} = 13 TeV, 10 fb^{-1}")
 
# Save the plot
c.SaveAs("df106_HiggsToFourLeptons.png")
print("Saved figure to df106_HiggsToFourLeptons.png")