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df104_HiggsToTwoPhotons.py File Reference

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 df104_HiggsToTwoPhotons
 

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The Higgs to two photons analysis from the ATLAS Open Data 2020 release, with RDataFrame.

This tutorial is the Higgs to two photons 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. Although the Higgs to two photons decay is very rare, the contribution of the Higgs can be seen as a narrow peak around 125 GeV because of the excellent reconstruction and identification efficiency of photons at the ATLAS experiment.

The analysis is translated to a RDataFrame workflow processing 1.7 GB of simulated events and data.

import ROOT
import os
# Enable multi-threading
ROOT.ROOT.EnableImplicitMT()
# Create a ROOT dataframe for each dataset
path = "root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22"
df = {}
df["data"] = ROOT.RDataFrame("mini", (os.path.join(path, "GamGam/Data/data_{}.GamGam.root".format(x)) for x in ("A", "B", "C", "D")))
df["ggH"] = ROOT.RDataFrame("mini", os.path.join(path, "GamGam/MC/mc_343981.ggH125_gamgam.GamGam.root"))
df["VBF"] = ROOT.RDataFrame("mini", os.path.join(path, "GamGam/MC/mc_345041.VBFH125_gamgam.GamGam.root"))
processes = list(df.keys())
# Apply scale factors and MC weight for simulated events and a weight of 1 for the data
for p in ["ggH", "VBF"]:
df[p] = df[p].Define("weight",
"scaleFactor_PHOTON * scaleFactor_PhotonTRIGGER * scaleFactor_PILEUP * mcWeight");
df["data"] = df["data"].Define("weight", "1.0")
# Select the events for the analysis
for p in processes:
# Apply preselection cut on photon trigger
df[p] = df[p].Filter("trigP")
# Find two good muons with tight ID, pt > 25 GeV and not in the transition region between barrel and encap
df[p] = df[p].Define("goodphotons", "photon_isTightID && (photon_pt > 25000) && (abs(photon_eta) < 2.37) && ((abs(photon_eta) < 1.37) || (abs(photon_eta) > 1.52))")\
.Filter("Sum(goodphotons) == 2")
# Take only isolated photons
df[p] = df[p].Filter("Sum(photon_ptcone30[goodphotons] / photon_pt[goodphotons] < 0.065) == 2")\
.Filter("Sum(photon_etcone20[goodphotons] / photon_pt[goodphotons] < 0.065) == 2")
# Compile a function to compute the invariant mass of the diphoton system
ROOT.gInterpreter.Declare(
"""
using Vec_t = const ROOT::VecOps::RVec<float>;
float ComputeInvariantMass(Vec_t& pt, Vec_t& eta, Vec_t& phi, Vec_t& 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]);
return (p1 + p2).mass() / 1000.0;
}
""")
# Define a new column with the invariant mass and perform final event selection
hists = {}
for p in processes:
# Make four vectors and compute invariant mass
df[p] = df[p].Define("m_yy", "ComputeInvariantMass(photon_pt[goodphotons], photon_eta[goodphotons], photon_phi[goodphotons], photon_E[goodphotons])")
# Make additional kinematic cuts and select mass window
df[p] = df[p].Filter("photon_pt[goodphotons][0] / 1000.0 / m_yy > 0.35")\
.Filter("photon_pt[goodphotons][1] / 1000.0 / m_yy > 0.25")\
.Filter("m_yy > 105 && m_yy < 160")
# Book histogram of the invariant mass with this selection
hists[p] = df[p].Histo1D(
ROOT.RDF.TH1DModel(p, "Diphoton invariant mass; m_{#gamma#gamma} [GeV];Events", 30, 105, 160),
"m_yy", "weight")
# Run the event loop
ggh = hists["ggH"].GetValue()
vbf = hists["VBF"].GetValue()
data = hists["data"].GetValue()
# Create the plot
# Set styles
ROOT.gROOT.SetStyle("ATLAS")
# Create canvas with pads for main plot and data/MC ratio
c = ROOT.TCanvas("c", "", 700, 750)
upper_pad = ROOT.TPad("upper_pad", "", 0, 0.35, 1, 1)
lower_pad = ROOT.TPad("lower_pad", "", 0, 0, 1, 0.35)
for p in [upper_pad, lower_pad]:
p.SetLeftMargin(0.14)
p.SetRightMargin(0.05)
p.SetTickx(False)
p.SetTicky(False)
upper_pad.SetBottomMargin(0)
lower_pad.SetTopMargin(0)
lower_pad.SetBottomMargin(0.3)
upper_pad.Draw()
lower_pad.Draw()
# Fit signal + background model to data
upper_pad.cd()
fit = ROOT.TF1("fit", "([0]+[1]*x+[2]*x^2+[3]*x^3)+[4]*exp(-0.5*((x-[5])/[6])^2)", 105, 160)
fit.FixParameter(5, 125.0)
fit.FixParameter(4, 119.1)
fit.FixParameter(6, 2.39)
fit.SetLineColor(2)
fit.SetLineStyle(1)
fit.SetLineWidth(2)
data.Fit("fit", "", "E SAME", 105, 160)
fit.Draw("SAME")
# Draw background
bkg = ROOT.TF1("bkg", "([0]+[1]*x+[2]*x^2+[3]*x^3)", 105, 160)
for i in range(4):
bkg.SetParameter(i, fit.GetParameter(i))
bkg.SetLineColor(4)
bkg.SetLineStyle(2)
bkg.SetLineWidth(2)
bkg.Draw("SAME")
# Draw data
data.SetMarkerStyle(20)
data.SetMarkerSize(1.2)
data.SetLineWidth(2)
data.SetLineColor(ROOT.kBlack)
data.Draw("E SAME")
data.SetMinimum(1e-3)
data.SetMaximum(8e3)
data.GetYaxis().SetLabelSize(0.045)
data.GetYaxis().SetTitleSize(0.05)
data.SetStats(0)
data.SetTitle("")
# Scale simulated events with luminosity * cross-section / sum of weights
# and merge to single Higgs signal
lumi = 10064.0
ggh.Scale(lumi * 0.102 / 55922617.6297)
vbf.Scale(lumi * 0.008518764 / 3441426.13711)
higgs = ggh.Clone()
higgs.Add(vbf)
higgs.Draw("HIST SAME")
# Draw ratio
lower_pad.cd()
ratiobkg = ROOT.TF1("zero", "0", 105, 160)
ratiobkg.SetLineColor(4)
ratiobkg.SetLineStyle(2)
ratiobkg.SetLineWidth(2)
ratiobkg.SetMinimum(-125)
ratiobkg.SetMaximum(250)
ratiobkg.GetXaxis().SetLabelSize(0.08)
ratiobkg.GetXaxis().SetTitleSize(0.12)
ratiobkg.GetXaxis().SetTitleOffset(1.0)
ratiobkg.GetYaxis().SetLabelSize(0.08)
ratiobkg.GetYaxis().SetTitleSize(0.09)
ratiobkg.GetYaxis().SetTitle("Data - Bkg.")
ratiobkg.GetYaxis().CenterTitle()
ratiobkg.GetYaxis().SetTitleOffset(0.7)
ratiobkg.GetYaxis().SetNdivisions(503, False)
ratiobkg.GetYaxis().ChangeLabel(-1, -1, 0)
ratiobkg.GetXaxis().SetTitle("m_{#gamma#gamma} [GeV]")
ratiobkg.Draw()
ratiosig = ROOT.TH1F("ratiosig", "ratiosig", 5500, 105, 160)
ratiosig.Eval(fit)
ratiosig.SetLineColor(2)
ratiosig.SetLineStyle(1)
ratiosig.SetLineWidth(2)
ratiosig.Add(bkg, -1)
ratiosig.Draw("SAME")
ratiodata = data.Clone()
ratiodata.Add(bkg, -1)
ratiodata.Draw("E SAME")
for i in range(1, data.GetNbinsX()):
ratiodata.SetBinError(i, data.GetBinError(i))
# Add legend
upper_pad.cd()
legend = ROOT.TLegend(0.55, 0.55, 0.89, 0.85)
legend.SetTextFont(42)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.05)
legend.SetTextAlign(32)
legend.AddEntry(data, "Data" ,"lep")
legend.AddEntry(bkg, "Background", "l")
legend.AddEntry(fit, "Signal + Bkg.", "l")
legend.AddEntry(higgs, "Signal", "l")
legend.Draw("SAME")
# Add ATLAS label
text = ROOT.TLatex()
text.SetNDC()
text.SetTextFont(72)
text.SetTextSize(0.05)
text.DrawLatex(0.18, 0.84, "ATLAS")
text.SetTextFont(42)
text.DrawLatex(0.18 + 0.13, 0.84, "Open Data")
text.SetTextSize(0.04)
text.DrawLatex(0.18, 0.78, "#sqrt{s} = 13 TeV, 10 fb^{-1}");
# Save the plot
c.SaveAs("HiggsToTwoPhotons.pdf");
FCN=19.9699 FROM HESSE STATUS=NOT POSDEF 23 CALLS 158 TOTAL
EDM=3.65047e-12 STRATEGY= 1 ERR MATRIX NOT POS-DEF
EXT PARAMETER APPROXIMATE STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 p0 9.43252e+04 7.20516e+01 2.24889e-02 8.93365e-09
2 p1 -1.77723e+03 7.78152e-01 4.23724e-04 -1.87354e-06
3 p2 1.15606e+01 5.36057e-03 2.75626e-06 -1.80941e-04
4 p3 -2.56282e-02 2.66823e-05 6.11023e-09 3.02615e-02
5 p4 1.19100e+02 fixed
6 p5 1.25000e+02 fixed
7 p6 2.39000e+00 fixed
Date
February 2020
Author
Stefan Wunsch (KIT, CERN)

Definition in file df104_HiggsToTwoPhotons.py.

ROOT::RDataFrame
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
Definition: RDataFrame.hxx:42
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
ROOT::RDF::TH1DModel
A struct which stores the parameters of a TH1D.
Definition: HistoModels.hxx:27