Aod ntuple

This page contains basic prescriptions to get physics objects from the AOD and the AOD-based Root ntuple (from now on defined as Woutuple).

Some comments on quality selection cuts will be added as work progresses.

Electron

Electron in AOD/Woutuple

AOD Container Name	AOD Variable	Woutuple Variable	Variable Type	Comment
ElectronCollection	(*elecTES)->size()	n_elec	Int	Number of electrons in the Woutuple
	(*elecItr)->hlv().x()	px_elec	Double	Px
	(*elecItr)->hlv().y()	py_elec	Double	Py
	(*elecItr)->hlv().z()	pz_elec	Double	Pz
	(*elecItr)->hlv().perp()	pt_elec	Double	Pt
	(*elecItr)->hlv().eta()	eta_elec	Double	Eta
	(*elecItr)->hlv().phi()	phi_elec	Double	Phi
	(*elecItr)->isEM()	isem_elec	Int	isEM flag (see below)
	(*elecItr)->hasTrack()	hastrk_elec	Int (bool)	HasTrack flag: presence of charged track in the Inner Detector
	(*elecItr)->z0wrtPrimVtx()	z0vtx_elec	Double	Intersection (z) of track with the beam axis
	(*elecItr)->d0wrtPrimVtx()	d0vtx_elec	Double	Transverse impact parameter d0
	(*elecItr)->numberOfBLayerHits()	nblayerhits_elec	Int	Number of hits in the Pixel B-layer
	(*elecItr)->numberOfPixelHits()	npixelhits_elec	Int	Number of hits in the Pixel detector
	(*elecItr)->numberOfSCTHits()	nscthits_elec	Int	Number of hits in the SCT
	(*elecItr)->numberOfTRTHits()	ntrthits_elec	Int	Number of hits in the TRT
	(*elecItr)->numberOfTRTHighThresholdHits()	ntrththits_elec	Int	Number of high threshold hits in the TRT
	(*elecItr)->author()	auth_elec	Int (enum)	Algorithm used to create the electron: unknown=0, egamma=1, softe=2
	(*elecItr)->parameter(ElectronParameters::EoverP)	eoverp_elec	Double	E/P ratio
	(*elecItr)->parameter(ElectronParameters::etcone)	etcone_elec	Double	Energy deposition in a cone dR=0.45 around the electron cluster
	(*elecItr)->parameter(ElectronParameters::etcone20)	etcone20_elec	Double	Energy deposition in a cone dR=0.20 around the electron cluster. Standard cone size for ATLFAST
	(*elecItr)->parameter(ElectronParameters::etcone30)	etcone30_elec	Double	Energy deposition in a cone dR=0.30 around the electron cluster. Currently empty
	(*elecItr)->parameter(ElectronParameters::etcone40)	etcone40_elec	Double	Energy deposition in a cone dR=0.40 around the electron cluster
	(*elecItr)->parameter(ElectronParameters::emWeight)	emwgt_elec	Double	Weight for electrons (see below)
	(*elecItr)->parameter(ElectronParameters::pionWeight)	piwgt_elec	Double	Weight for pions (see below)

There are 3 types of quality cuts you can perform on the electron candidates:

1. Cuts based on the isEM flag
2. Cuts based on likelihood
3. Cuts based on NeuralNet output

1. The isEM flag uses both calorimeter and tracking information in addition to TRT information. The flag is a bit field which marks whether the candidate passed or not some safety checks.

The bit field marks the following checks:

  Cluster based egamma
  ClusterEtaRange        =  0,
  ClusterHadronicLeakage =  1,
  ClusterMiddleSampling  =  2,
  ClusterFirstSampling   =  3,
  Track based egamma
  TrackEtaRange          =  8,
  TrackHitsA0            =  9,
  TrackMatchAndEoP       = 10,
  TrackTRT               = 11

In 9.0.4 there is a problem with TRT simulation so one has to mask TRT bit to recover the lost efficiency.

To get the flag in your AOD analysis you should use:

(*elec)->isEM()

To mask the TRT bits you should use: (*elec)->isEM()&0x7FF==0

If you use isEM then you will select electrons with an overall efficiency of about 80% in the barrel but much lower in the crack and endcap.

2. The likelihood ratio is constructed using the following variables: energy in different calorimeter samplings, shower shapes in both eta and phi and E/P ration. No TRT information is used here. You need to access two variables called emweight and pionweight then you can construct the likelihood ratio, defined by: emweight/(emweight+pionweight).

In AOD, you use the following code:

ElecEMWeight = elec*->parameter(ElectronParameters::emWeight); ElecPiWeight = elec*->parameter(ElectronParameters::pionWeight);

Then form the variable: X = ElecEMWeight/(ElecEMWeight+ElecPiWeight);

Requiring X > 0.6 will give you more than 90% efficiency for electrons.

3. The NeuralNet variable uses as inputs the same variables used for likelihood. To use it in AOD you should proceed as follow:

ElecepiNN = elec*->parameter(ElectronParameters::epiNN);

Requiring ElecepiNN > 0.6 will give you about 90% eff for electrons.

However, you should be aware that the NN was trained in full eta range while the likelihood was computed in 3 bins in eta: barrel, crack and endcap. So I would suggest to use likelihood for now.

To require an isolated electron, you have to cut on the energy deposited in the cone around the electron cluster. ATLFAST for example requires Et<10 GeV in a cone of dR=0.2. You can simulate the ATLFAST cut by requiring etcone20<10.*GeV

Muon

Muon in AOD/Woutuple

AOD Container Name	AOD Variable	Woutuple Variable	Variable Type	Comment
MuonCollection	(*muonTES)->size()	n_muon	Int	Number of muons in the Woutuple
	(*muonItr)->hlv().x()	px_muon	Double	Px
	(*muonItr)->hlv().y()	py_muon	Double	Py
	(*muonItr)->hlv().z()	pz_muon	Double	Pz
	(*muonItr)->hlv().perp()	pt_muon	Double	Pt
	(*muonItr)->hlv().eta()	eta_muon	Double	Eta
	(*muonItr)->hlv().phi()	phi_muon	Double	Phi
	(*muonItr)->author()	auth_muon	Int (enum)	Algorithm used to create the muon: unknown=0, highPt=1, lowPt=2
	(*muonItr)->chi2()	chi2_muon	Double	Chi2 of the track fit. Empty for now (see below)
	(*muonItr)->getConeIsol()[0]	coneiso0_muon	Double
	(*muonItr)->getEtIsol()[0]	etiso0_muon	Double
	(*muonItr)->hasCombinedMuon()	hascombi_muon	Int (bool)
	(*muonItr)->hasInDetTrackParticle()	hasindettp_muon	Int (bool)
	(*muonItr)->hasMuonSpectrometerTrackParticle()	hasmuspectp_muon	Int (bool)
	(*muonItr)->hasMuonExtrapolatedTrackParticle()	hasmmuextrtp_muon	Int (bool)
	(*muonItr)->hasCombinedMuonTrackParticle()	hascombimutp_muon	Int (bool)
	(*muonItr)->hasCluster()	hasclus_muon	Int (bool)
	(*muonItr)->isHighPt()	ishipt_muon	Int (bool)	Is the muon produced with the highPt algorithm? (see also author())
	(*muonItr)->isLowPt()	islopt_muon	Int (bool)	Is the muon produced with the lowPt algorithm? (see also author())
	(*muonItr)->numberOfBLayerHits()	nblayerhits_muon	Int
	(*muonItr)->numberOfPixelHits(	npixelhits_muon	Int
	(*muonItr)->numberOfSCTHits()	nscthits_muon	Int
	(*muonItr)->numberOfTRTHits()	ntrthits_muon	Int
	(*muonItr)->numberOfMDTHits()	nmdthits_muon	Int
	(*muonItr)->numberOfCSCEtaHits()	ncscetahits_muon	Int
	(*muonItr)->numberOfCSCPhiHits()	ncscphihits_muon	Int
	(*muonItr)->numberOfRPCEtaHits()	nrpcetahits_muon	Int
	(*muonItr)->numberOfRPCPhiHits(	nrpcphihits_muon	Int
	(*muonItr)->numberOfTGCEtaHits()	ntgcetahits_muon	Int
	(*muonItr)->numberOfTGCPhiHits()	ntgcphihits_muon	Int
	(*muonItr)->z0wrtPrimVtx()	z0vtx_muon	Double
	(*muonItr)->d0wrtPrimVtx()	d0vtx_muon	Double

temporary: The muons have highPt and lowPt algorithms. The overlap is removed, but you may want to only use the highPt ones. The chi2() method is always 0 in 10.0.1, so you will have to access the CombinedMuon through something like

 const Rec::TrackParticle* cbndMuon = part->get_CombinedMuonTrackParticle();
 if( cbndMuon ) {
   double chi2 = cbndMuon->fitQuality()->chiSquared();
   int ndof =  cbndMuon->fitQuality()->numberDoF();
   if( ndof > 0 ) chi2 = chi2/ndof;
   return chi2;
 }

Jet

There are three collections of jets:

cone R=.4, cone R=.7, and Kt (with parameter =1).

External References

Container/Object Names for AOD

Storegate Keys For AOD 10.x

Particle Preselection Cuts

Electron.h Include File

Muon.h Include File