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df105_WBosonAnalysis.py File Reference

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 df105_WBosonAnalysis
 

Detailed Description

The W boson mass analysis from the ATLAS Open Data release of 2020, with RDataFrame.

View in nbviewer Open in SWAN This tutorial is the analysis of the W boson mass taken from the ATLAS Open Data release in 2020 (http://opendata.atlas.cern/release/2020/documentation/). The data was recorded with the ATLAS detector during 2016 at a center-of-mass energy of 13 TeV. W bosons are produced frequently at the LHC and are an important background to studies of Standard Model processes, for example the Higgs boson analyses.

The analysis is translated to a RDataFrame workflow processing a subset of 60 GB of simulated events and data. The original analysis is reduced by a factor given as the command line argument –lumi-scale.

import ROOT
import json
import argparse
import os
# Argument parsing
parser = argparse.ArgumentParser()
parser.add_argument("--lumi-scale", default=0.01, help="Scale of the overall lumi of 10 fb^-1")
parser.add_argument("-b", action="store_true", default=False, help="Use ROOT batch mode")
args = parser.parse_args()
if args.b: ROOT.gROOT.SetBatch(True)
# Create a ROOT dataframe for each dataset
# Note that we load the filenames from an external json file.
path = "root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22"
files = json.load(open(os.path.join(os.environ["ROOTSYS"], "tutorials/dataframe", "df105_WBosonAnalysis.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
num_events = d[4] # Number of events
samples.append(sample)
df[sample] = ROOT.RDataFrame("mini", "{}/1lep/{}/{}.1lep.root".format(path, folder, sample))
# Scale down the datasets
df[sample] = df[sample].Range(int(num_events * args.lumi_scale))
# Select events for the analysis
ROOT.gInterpreter.Declare("""
bool GoodElectronOrMuon(int type, float pt, float eta, float phi, float e, float trackd0pv, float tracksigd0pv, float z0)
{
ROOT::Math::PtEtaPhiEVector p(pt / 1000.0, eta, phi, e / 1000.0);
if (abs(z0 * sin(p.theta())) > 0.5) return false;
if (type == 11 && abs(eta) < 2.46 && (abs(eta) < 1.37 || abs(eta) > 1.52)) {
if (abs(trackd0pv / tracksigd0pv) > 5) return false;
return true;
}
if (type == 13 && abs(eta) < 2.5) {
if (abs(trackd0pv / tracksigd0pv) > 3) return false;
return true;
}
return false;
}
""")
for s in samples:
# Require missing transverse energy larger than 30 GeV
df[s] = df[s].Filter("met_et > 30000")
# Select electron or muon trigger
df[s] = df[s].Filter("trigE || trigM")
# Select events with exactly one good lepton
df[s] = df[s].Define("good_lep", "lep_isTightID && lep_pt > 35000 && lep_ptcone30 / lep_pt < 0.1 && lep_etcone20 / lep_pt < 0.1")\
.Filter("Sum(good_lep) == 1")
# Apply additional cuts in case the lepton is an electron or muon
df[s] = df[s].Define("idx", "ROOT::VecOps::ArgMax(good_lep)")\
.Filter("GoodElectronOrMuon(lep_type[idx], lep_pt[idx], lep_eta[idx], lep_phi[idx], lep_E[idx], lep_trackd0pvunbiased[idx], lep_tracksigd0pvunbiased[idx], lep_z0[idx])")
# 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 transverse mass of the W boson using the lepton and the missing transverse energy and make a histogram
ROOT.gInterpreter.Declare("""
float ComputeTransverseMass(float met_et, float met_phi, float lep_pt, float lep_eta, float lep_phi, float lep_e)
{
ROOT::Math::PtEtaPhiEVector met(met_et, 0, met_phi, met_et);
ROOT::Math::PtEtaPhiEVector lep(lep_pt, lep_eta, lep_phi, lep_e);
return (met + lep).Mt() / 1000.0;
}
""")
histos = {}
for s in samples:
df[s] = df[s].Define("mt_w", "ComputeTransverseMass(met_et, met_phi, lep_pt[idx], lep_eta[idx], lep_phi[idx], lep_E[idx])")
histos[s] = df[s].Histo1D(ROOT.RDF.TH1DModel(s, "mt_w", 40, 60, 180), "mt_w", "weight")
# Run the event loop and merge histograms of the respective processes
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")
wjets = merge_histos("wjets")
zjets = merge_histos("zjets")
ttbar = merge_histos("ttbar")
diboson = merge_histos("diboson")
singletop = merge_histos("singletop")
# 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.SetLogy()
pad.Draw()
pad.cd()
# Draw stack with MC contributions
stack = ROOT.THStack()
for h, color in zip(
[singletop, diboson, ttbar, zjets, wjets],
[(208, 240, 193), (195, 138, 145), (155, 152, 204), (248, 206, 104), (222, 90, 106)]):
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_{T}^{W#rightarrow l#nu} [GeV]")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetLabelSize(0.04)
stack.GetYaxis().SetTitleSize(0.045)
stack.SetMaximum(1e10 * args.lumi_scale)
stack.SetMinimum(1e1)
# 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(wjets, "W+jets", "f")
legend.AddEntry(zjets, "Z+jets", "f")
legend.AddEntry(ttbar, "t#bar{t}", "f")
legend.AddEntry(diboson, "Diboson", "f")
legend.AddEntry(singletop, "Single top", "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, {:.1f} fb^{{-1}}".format(lumi * args.lumi_scale / 1000.0))
# Save the plot
c.SaveAs("WBosonAnalysis.pdf")
Date
March 2020
Author
Stefan Wunsch (KIT, CERN)

Definition in file df105_WBosonAnalysis.py.

Range
Ta Range(0, 0, 1, 1)
ROOT::RDataFrame
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
Definition: RDataFrame.hxx:42
ROOT::Math::detail::open
@ open
Definition: GenVectorIO.h:55
ROOT::VecOps::Filter
RVec< T > Filter(const RVec< T > &v, F &&f)
Create a new collection with the elements passing the filter expressed by the predicate.
Definition: RVec.hxx:938
int
ROOT::RDF::TH1DModel
A struct which stores the parameters of a TH1D.
Definition: HistoModels.hxx:27