Aod ntuple

This page contains basic prescriptions to get physics objects from the AOD and the AOD-based Root ntuple.

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

Electron

Electron in AOD/Ntuple

AOD Container Name	AOD Variable	Ntuple Variable	Variable Type	Comment
ElectronCollection
			n_elec	Int	Number of electrons in the Ntuple
			px_elec	Double	Px
			py_elec	Double	Py
			pz_elec	Double	Pz
			pt_elec	Double	Pt
			eta_elec	Double	Eta
			phi_elec	Double	Phi
			isem_elec	Int	isEM flag (see below)
			hastrk_elec	Int (bool)	HasTrack flag: presence of charged track in the Inner Detector
			z0vtx_elec	Double	Intersection (z) of track with the beam axis
			d0vtx_elec	Double	Transverse impact paramter d0
			nblayerhits_elec	Int	Number of hits in the Pixel B-layer
			npixelhits_elec	Int	Number of hits in the Pixel detector
			nscthits_elec	Int	Number of hits in the SCT
			ntrthits_elec	Int	Number of hits in the TRT
			ntrththits_elec	Int	Number of high threshold hits in the TRT
		Int_t auth_elec[1000]; //[nelec]
		Double_t eoverp_elec[1000]; //[nelec]
		Double_t etcone_elec[1000]; //[nelec]
		Double_t etcone20_elec[1000]; //[nelec]
		Double_t etcone30_elec[1000]; //[nelec]
		Double_t etcone40_elec[1000]; //[nelec]
		Double_t emwgt_elec[1000]; //[nelec]
		Double_t piwgt_elec[1000]; //[nelec]

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.

External References

Container/Object Names for AOD

Particle Preselection Cuts