First you need to produce the ntuple from GJets datasets, with photon variables needed for the MVA training. The example dumper can be found inside flashgg area.
Three GJet datasets are normally used.
- For standard Higgs to diphoton analysis
- GJet_Pt-20to40_DoubleEMEnriched_MGG-80toInf_TuneCUETP8M1_13TeV_Pythia8
- GJet_Pt-40toInf_DoubleEMEnriched_MGG-80toInf_TuneCUETP8M1_13TeV_Pythia8
- For low mass Higgs analysis
- GJet_Pt-20toInf_DoubleEMEnriched_MGG-40to80_TuneCUETP8M1_13TeV_Pythia8
You need to write a json with the available GJet datasets to run the jobs in batch using script fggRunJobs.py
Once you got the ntuple, you need to do a 2D reweigt (pt and eta) to prompt photons with respect to fake photons. We usually do it in three steps.
- To Produce the 2D weight factors
root -l create2Dweights.cc
It will produce a root file with the weights which we need to use in the next step. 2. To add weights
root -l add2Dweights.cc
This step also splits the tree into half for training purpose. Normally we take the odd events for training purpose. There is also a similar script which basically do the same thing except it saves the even events for test purpose.
root -l makeTestTree.cc
- Now you need to produce the plots to check if the reweighting has been done successfully.
root -l check2Dweights.cc
By default it produces plots only for barrel. For endcap you need to run again by modifying a few variable inside the scripts.
For the TMVA training there are two scripts,
- For barrel: TMVAClassification_preSelCuts_Run2_EB.C
- For endcap: TMVAClassification_preSelCuts_Run2_EE.C
Once you are done with training, you need to produce the weight files from the output root files using the following scripts.
- For barrel: evaluateMVA_EB_Hgg_newMC.cc
- For endcap: evaluateMVA_EE_Hgg_newMC.cc
To plot the MVA output and ROC curves, use
- plotMVARes_barrel_Hgg_newMC.cc (for barrel)
- plotMVARes_endcap_Hgg_newMC.cc (for endcap)
To plot efficiency with respect to pT, no. of vertices and eta, use createHistoPhoEff_Hgg_2.cc.