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.

--Barison 18:12, 19 May 2005 (MET DST)

Truth Particles

The ntuples contain the full MC truth information that is contained in the AOD. For each particle the 4-vector, PDG code, status code and decay tree navigation information: references to decay products of any given truth particle are stored in tidx_dg0_truth through tidx_dg9_truth, which contain the indeces of the decay products in the truth ntuple block. The actual number of daughters for a given decay is specified by numdg_truth. tidx_moth_truth contains the index of the mother particle in the truth ntuple block. If a given link does not exists (e.g. a particle has no mother), then the link index value is set to -1. For convenience, direct 'jump links' exist to the top quark and anti-top quark in the event (if present) in tidx_top_truth and tidx_antitop_truth. Direct jump links to the decay producs of the top (a W and a b quark) are also stored in tidx_Wplus_truth, tidx_Wminus_truth, tidx_b_truth and tidx_antib_truth

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:

Cuts based on the isEM flag
Cuts based on likelihood
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

Photon

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;
 }

Tau

Jet

There are three collections of jets which are all stored in the ntuple. The ntuple variable algo_jet tells you which jet clustering algorithm created the jet. Be sure to cut on algo_jet when you loop over the jets, otherwise you'll see many jets 3 times.

KtTowerParticleJets (Kt algorithm with parameter D=1, algo_jet=0)
Cone4TowerParticleJets (Cone algorithm with R=0.4, algo_jet=1)
ConeTowerParticleJets (Cone algorithm with R=0.7, algo_jet=2)

All three alogrithms have a common data structure.

You can get the energy contributions to a jet from different calorimeter samplings. Each calorimeter (Barrel and Endcap) has up to 4 samplings:

PreSamplerB, EMB1, EMB2, EMB3,    //   LAr barrel
PreSamplerE, EME1, EME2, EME3,    //   LAr EM endcap  
HEC0, HEC1, HEC2, HEC3,           //   Hadronic end cap cal. 
TileBar0, TileBar1, TileBar2,     //   Tile barrel 
TileGap1, TileGap2, TileGap3,     //   Tile gap (ITC & scint) 
TileExt0, TileExt1, TileExt2,     //   Tile extended barrel 
FCAL0, FCAL1, FCAL2,              //   Forward EM endcap

Overlapping jets and fake jets: A trick used in CDF is to neglect jets which are close to an Electron (typically dR<0.7). The same trick should be applied for Photons and Taus.

**Jet in AOD/Woutuple**
AOD Container Name	AOD Variable	Woutuple Variable	Variable Type	Comment
KtTowerParticleJets Cone4TowerParticleJets ConeTowerParticleJets	`(*jetTES)->size()`	n_jet	Int	Number of jets in the Woutuple
	`(*jetItr)->hlv().x()`	px_jet	Double	Px
	`(*jetItr)->hlv().y()`	py_jet	Double	Py
	`(*jetItr)->hlv().z()`	pz_jet	Double	Pz
	`(*jetItr)->hlv().perp()`	pt_jet	Double	Pt
	`(*jetItr)->hlv().eta()`	eta_jet	Double	Eta
	`(*jetItr)->hlv().phi()`	phi_jet	Double	Phi
	`(*jetItr)->pCalo().x()`	px_calo_jet	Double
	`(*jetItr)->pCalo().y()`	py_calo_jet	Double
	`(*jetItr)->pCalo().z()`	pz_calo_jet	Double
	`(*jetItr)->etEM(0)`	etem0_jet	Double	Et from EM_Calo Sample_1(B+E) and Presampler (B+E)
	`(*jetItr)->etEM(1)`	etem1_jet	Double	Et from EM_Calo Sample_2(B+E)
	`(*jetItr)->etEM(2)`	etem2_jet	Double	Et from EM_Calo Sample_3(B+E)
	`(*jetItr)->etHad(0)`	ethad0_jet	Double	Et from HAD_Calo Sample_0(B+E)
	`(*jetItr)->etHad(1)`	ethad1_jet	Double	Et from HAD_Calo Sample_1(B+E)
	`(*jetItr)->etHad(2)`	ethad2_jet	Double	Et from HAD_Calo Sample_2(B+E)
	`(*jetItr)->energyInSample(CaloSampling::PreSamplerB)`	epresb_jet	Double	Energy in PreSampler (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::EMB1)`	eemb1_jet	Double	Energy in EM_Calo Sample_1 (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::EMB2)`	eemb2_jet	Double	Energy in EM_Calo Sample_2 (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::EMB3)`	eemb3_jet	Double	Energy in EM_Calo Sample_3 (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::PreSamplerE)`	eprese_jet	Double	Energy in PreSampler (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::EME1)`	eeme1_jet	Double	Energy in EM_Calo Sample_1 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::EME2)`	eeme2_jet	Double	Energy in EM_Calo Sample_2 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::EME3)`	eeme3_jet	Double	Energy in EM_Calo Sample_3 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::HEC0)`	ehec0_jet	Double	Energy in HAD_Calo Sample_0 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::HEC1)`	ehec1_jet	Double	Energy in HAD_Calo Sample_1 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::HEC2)`	ehec2_jet	Double	Energy in HAD_Calo Sample_2 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::HEC3)`	ehec3_jet	Double	Energy in HAD_Calo Sample_3 (Endcap)
	`(*jetItr)->energyInSample(CaloSampling::TileBar0)`	etilebar0_jet	Double	Energy in HAD_Calo Sample_0 (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::TileBar1)`	etilebar1_jet	Double	Energy in HAD_Calo Sample_1 (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::TileBar2)`	etilebar2_jet	Double	Energy in HAD_Calo Sample_2 (Barrel)
	`(*jetItr)->energyInSample(CaloSampling::TileGap1)`	etilegap1_jet	Double	Energy in Tile_gap Sample_1
	`(*jetItr)->energyInSample(CaloSampling::TileGap2)`	etilegap2_jet	Double	Energy in Tile_gap Sample_2
	`(*jetItr)->energyInSample(CaloSampling::TileGap3)`	etilegap3_jet	Double	Energy in Tile_gap Sample_3
	`(*jetItr)->energyInSample(CaloSampling::TileExt0)`	etileext0_jet	Double	Energy in Tile Extended Barrel Sample_0
	`(*jetItr)->energyInSample(CaloSampling::TileExt1)`	etileext1_jet	Double	Energy in Tile Extended Barrel Sample_1
	`(*jetItr)->energyInSample(CaloSampling::TileExt2)`	etileext2_jet	Double	Energy in Tile Extended Barrel Sample_2
	`(*jetItr)->energyInSample(CaloSampling::FCAL0)`	efcal0_jet	Double	Energy in FCAL Sample_0
	`(*jetItr)->energyInSample(CaloSampling::FCAL1)`	efcal1_jet	Double	Energy in FCAL Sample_1
	`(*jetItr)->energyInSample(CaloSampling::FCAL2)`	efcal2_jet	Double	Energy in FCAL Sample_2
	`(*jetItr)->energyInSample(CaloSampling::Unknown)`	eunknown_jet	Double	WTF???
	`(*jetItr)->energyInCryostat() ;`	ecryo_jet	Double	Energy lost in the cryostat — empiric evaluation sqrt(EMB3 * TileBar0)

Truth Jets

Truth jets are formed by running the jet reconstruction algorithm on final Truth particles from the simulation. Jets created this way do not contain the effects of detector energy resolution and other experimental issues. The Woutuple contains truth jets generated with the same three jet algorithms as for reconstruction level jets (Cone,Cone4 and Kt). The author of each jet in the trujet block can be determined from the algo_trujet integer, which has the same meaning as the algo_jet variable in the reco-level jet block

BJet

The default b-tagging algorithm is run on cone jets with R=0.7

It is possible to run a b-tagging refit completely on AOD. Andi Wildauer has done a lot of work on this and documented it on:

BTagging refit on AOD

Missing Et

There are seven(!) Missing Et objects available in AOD. The Woutuple contains all of them

**Missing Et objects available in AOD**
Type	AOD Container Name	Include File	Comment
Missing Et	MET_Base	MissingEtCalo.h	uncalibrated ETMiss
Missing Et calibrated	MET_Calib	MissingEtCalo.h	calibrated ETMiss
Missing Et Truth	MET_Truth	MissingEtTruth.h	ETMiss from Truth
Missing Et Muon	MET_Muon	MissingET.h	ETMiss from Muons
Missing Et Final	MET_Final		ETMiss for Physics analysis : calib+muons+cryostat correction
Missing Et Cryostat correction	MET_Cryo		Cryostat term
Missing Et Topological Clusters	MET_Topo		ETMiss from Topological Jet Clusters

The calibration of the MET is obtained by using a H1 algorithm. This algorithm corrects the cell energy as a function of the energy density in the cell: $weight={\frac {\ln \left(|E_{cell}/V|\right)}{\ln(2)}}+26$

The weights are stored in a lookup table here: Code for H1 calibration

**Missing Et objects available in Woutuple**
AOD Container Name	AOD Variable	Woutuple Variable	Variable Type	Comment
MET_Base		et_base_etm	Double_t	Missing Et (uncalibrated)
		ht_base_etm	Double_t	Total Ht (uncalibrated)
		px_base_etm	Double_t	Missing Px (uncalibrated)
		py_base_etm	Double_t	Missing Py (uncalibrated)
MET_Calib		et_calib_etm	Double_t	Missing Et (calibrated)
		ht_calib_etm	Double_t	Total Ht (calibrated)
		px_calib_etm	Double_t	Missing Px (calibrated)
		py_calib_etm	Double_t	Missing Py (calibrated)