KU CMS EWkinos Jets

*Set ALLOWTOPICVIEW = KansasCMSGroup, CMSwebAccessSimbaGroup <br /> * Set ALLOWTOPICCHANGE = KansasCMSGroup

Table of Contents

SUSY nanoAOD Jet Variables

• Jets are corrected to latest available JECs and provided with latest available JetID, PU ID, b-tag and QGL.
• No systematic variations of JECs are stored, but the quantities (rho, area, pt, eta) needed to recompute JECs or JEC systematic variations are available. Dedicated nanoAOD-tools modules exist to compute those quantities.
• b-jet energy regression, developed for final states with one or more H->bb, is also available.
• Jets of MC samples are not smeared (dedicated nanoAOD-tools modules exist to apply the smearing)

Var Name	Type	Description
nFatJet	UInt_t	slimmedJetsAK8, i.e. ak8 fat jets for boosted analysis
FatJet _area	Float_t	jet catchment area, for JECs
FatJet _eta	Float_t	eta
FatJet _mass	Float_t	mass
FatJet _msoftdrop	Float_t	Corrected soft drop mass with PUPPI
FatJet _n2b1	Float_t	N2 with beta=1
FatJet _n3b1	Float_t	N3 with beta=1
FatJet _phi	Float_t	phi
FatJet _pt	Float_t	pt
FatJet _rawFactor	Float_t	1 - Factor to get back to raw pT
FatJet _tau1	Float_t	Nsubjettiness (1 axis)
FatJet _tau2	Float_t	Nsubjettiness (2 axis)
FatJet _tau3	Float_t	Nsubjettiness (3 axis)
FatJet _tau4	Float_t	Nsubjettiness (4 axis)
FatJet _jetId	Int_t	Jet ID flags bit1 is loose (always false in 2017 since it does not exist), bit2 is tight, bit3 is tightLepVeto
FatJet _subJetIdx1	Int_t	index of first subjet
FatJet _subJetIdx2	Int_t	index of second subjet
FatJet _btagCSVV2	Float_t	pfCombinedInclusiveSecondaryVertexV2 b-tag discriminator (aka CSVV2)
FatJet _btagDDBvL	Float_t	DeepDoubleX (mass-decorrelated) discriminator for H(Z)->bb vs QCD
FatJet _btagDeepB	Float_t	DeepCSV b+bb tag discriminator
FatJet _btagHbb	Float_t	Higgs to BB tagger discriminator
FatJet _deepTagMD_H4qvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger H->4q vs QCD discriminator
FatJet _deepTagMD_HbbvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger H->bb vs QCD discriminator
FatJet _deepTagMD_TvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger top vs QCD discriminator
FatJet _deepTagMD_WvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger W vs QCD discriminator
FatJet _deepTagMD_ZHbbvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger Z/H->bb vs QCD discriminator
FatJet _deepTagMD_ZHccvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger Z/H->cc vs QCD discriminator
FatJet _deepTagMD_ZbbvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger Z->bb vs QCD discriminator
FatJet _deepTagMD_ZvsQCD	Float_t	Mass-decorrelated DeepBoostedJet tagger Z vs QCD discriminator
FatJet _deepTagMD_bbvsLight	Float_t	Mass-decorrelated DeepBoostedJet tagger Z/H/gluon->bb vs light flavour discriminator
FatJet _deepTagMD_ccvsLight	Float_t	Mass-decorrelated DeepBoostedJet tagger Z/H/gluon->cc vs light flavour discriminator
FatJet _deepTag_TvsQCD	Float_t	DeepBoostedJet tagger top vs QCD discriminator
FatJet _deepTag_WvsQCD	Float_t	DeepBoostedJet tagger W vs QCD discriminator
FatJet _deepTag_ZvsQCD	Float_t	DeepBoostedJet tagger Z vs QCD discriminator

Var Name	Type	Description
nGenJetAK8	UInt_t	slimmedGenJetsAK8, i.e. ak8 Jets made with visible genparticles
GenJetAK8 _eta	Float_t	eta
GenJetAK8 _mass	Float_t	mass
GenJetAK8 _phi	Float_t	phi
GenJetAK8 _pt	Float_t	pt
GenJetAK8 _hadronFlavour	UChar_t	flavour from hadron ghost clustering
GenJetAK8 _partonFlavour	Int_t	flavour from parton matching

Var Name	Type	Description
nGenJet	UInt_t	slimmedGenJets, i.e. ak4 Jets made with visible genparticles
GenJet _eta	Float_t	eta
GenJet _mass	Float_t	mass
GenJet _phi	Float_t	phi
GenJet _pt	Float_t	pt
GenJet _hadronFlavour	UChar_t	flavour from hadron ghost clustering
GenJet _partonFlavour	Int_t	flavour from parton matching

Var Name	Type	Description
nSubGenJetAK8	UInt_t	slimmedGenJetsAK8SoftDropSubJets, i.e. subjets of ak8 Jets made with visible genparticles
SubGenJetAK8 _eta	Float_t	eta
SubGenJetAK8 _mass	Float_t	mass
SubGenJetAK8 _phi	Float_t	phi
SubGenJetAK8 _pt	Float_t	pt
Generator_binvar	Float_t	MC generation binning value

Var Name	Type	Description
nIsoTrack	UInt_t	isolated tracks after basic selection (((pt>5 && (abs(pdgId) == 11 or abs(pdgId) = 13)) or pt > 10) && (abs(pdgId) < 15 or abs(eta) < 2.5) && abs(dxy) < 0.2 && abs(dz) < 0.1 && ((pfIsolationDR03().chargedHadronIso < 5 && pt < 25) or pfIsolationDR03().chargedHadronIso/pt < 0.2)) and lepton veto
IsoTrack _dxy	Float_t	dxy (with sign) wrt first PV, in cm
IsoTrack _dz	Float_t	dz (with sign) wrt first PV, in cm
IsoTrack _eta	Float_t	eta
IsoTrack _pfRelIso03_all	Float_t	PF relative isolation dR=0.3, total (deltaBeta corrections)
IsoTrack _pfRelIso03_chg	Float_t	PF relative isolation dR=0.3, charged component
IsoTrack _phi	Float_t	phi
IsoTrack _pt	Float_t	pt
IsoTrack _miniPFRelIso_all	Float_t	mini PF relative isolation, total (with scaled rho*EA PU corrections)
IsoTrack _miniPFRelIso_chg	Float_t	mini PF relative isolation, charged component
IsoTrack _fromPV	Int_t	isolated track comes from PV
IsoTrack _pdgId	Int_t	PDG id of PF cand
IsoTrack _isFromLostTrack	Bool_t	if isolated track comes from a lost track
IsoTrack _isHighPurityTrack	Bool_t	track is high purity
IsoTrack _isPFcand	Bool_t	if isolated track is a PF candidate

Var Name	Type	Description
nJet	UInt_t	slimmedJets, i.e. ak4 PFJets CHS with JECs applied, after basic selection (pt > 15)
Jet_area	Float_t	jet catchment area, for JECs
Jet_btagCMVA	Float_t	CMVA V2 btag discriminator
Jet_btagCSVV2	Float_t	pfCombinedInclusiveSecondaryVertexV2 b-tag discriminator (aka CSVV2)
Jet_btagDeepB	Float_t	DeepCSV b+bb tag discriminator
Jet_btagDeepC	Float_t	DeepCSV charm btag discriminator
Jet_btagDeepFlavB	Float_t	DeepFlavour b+bb+lepb tag discriminator
Jet_chEmEF	Float_t	charged Electromagnetic Energy Fraction
Jet_chHEF	Float_t	charged Hadron Energy Fraction
Jet_eta	Float_t	eta
Jet_mass	Float_t	mass
Jet_muEF	Float_t	muon Energy Fraction
Jet_neEmEF	Float_t	neutral Electromagnetic Energy Fraction
Jet_neHEF	Float_t	neutral Hadron Energy Fraction
Jet_phi	Float_t	phi
Jet_pt	Float_t	pt
Jet_qgl	Float_t	Quark vs Gluon likelihood discriminator
Jet_rawFactor	Float_t	1 - Factor to get back to raw pT
Jet_bRegCorr	Float_t	pt correction for b-jet energy regression
Jet_bRegRes	Float_t	res on pt corrected with b-jet regression
Jet_electronIdx1	Int_t	index of first matching electron
Jet_electronIdx2	Int_t	index of second matching electron
Jet_jetId	Int_t	Jet ID flags bit1 is loose (always false in 2017 since it does not exist), bit2 is tight, bit3 is tightLepVeto
Jet_muonIdx1	Int_t	index of first matching muon
Jet_muonIdx2	Int_t	index of second matching muon
Jet_nConstituents	Int_t	Number of particles in the jet
Jet_nElectrons	Int_t	number of electrons in the jet
Jet_nMuons	Int_t	number of muons in the jet
Jet_puId	Int_t	Pilup ID flags
Jet_genJetIdx	Int_t	index of matched gen jet
Jet_hadronFlavour	Int_t	flavour from hadron ghost clustering
Jet_partonFlavour	Int_t	flavour from parton matching

Var Name	Type	Description
nSubJet	UInt_t	slimmedJetsAK8, i.e. ak8 fat jets for boosted analysis
SubJet _btagCMVA	Float_t	CMVA V2 btag discriminator
SubJet _btagCSVV2	Float_t	pfCombinedInclusiveSecondaryVertexV2 b-tag discriminator (aka CSVV2)
SubJet _btagDeepB	Float_t	DeepCSV b+bb tag discriminator
SubJet _eta	Float_t	eta
SubJet _mass	Float_t	mass
SubJet _n2b1	Float_t	N2 with beta=1
SubJet _n3b1	Float_t	N3 with beta=1
SubJet _phi	Float_t	phi
SubJet _pt	Float_t	pt
SubJet _rawFactor	Float_t	1 - Factor to get back to raw pT
SubJet _tau1	Float_t	Nsubjettiness (1 axis)
SubJet _tau2	Float_t	Nsubjettiness (2 axis)
SubJet _tau3	Float_t	Nsubjettiness (3 axis)
SubJet _tau4	Float_t	Nsubjettiness (4 axis)

StopTuplerMaker Jet variables

AK4 Jet Collection from 'slimmedJets' with additional jets from 'patJetsAK4PFCHS', w/ pt<10 and dr < 1.0, that match to a jet in one of the following genjet collections:

prodGenInfo:WtauemuVec

prodGenInfo:genDecayMomRefVec

prodGenInfo:WtauVec

prodGenInfo:WtaunuVec

prodElectronsNoIso:elesLVec

prodGenInfo:genDecayLVec

prodGenInfo:genDecayMomRefVec

AK8 Jet Collection from 'selectedPatJetsAK8PFPuppi' Puppi SubJets Collection from "selectedPatJetsAK8PFPuppiSoftDropPacked"

"No Lepton" collections start with PF Candidates matched to the primary vertex. Using using the pfCandidates collection with CandPtrProjector a new collection first where any candidates produced by prodMuons are vetoed is created and then the same process is done using the no Muon collection where candidates produced by prodElectrons are vetoed to create a no lepton collection. After this the remaining candidates are used to create, first, a no lepton CHS AK4 collection via the addJetCollection. The ak4chs no lepton collection then has a cut of pt > 10 applies.. The no lepton PUPPI AK8 collection and a PUPPI AK8 subject collection is created using the jetToolbox.

Filters

Type	Filter Name	Description
Int_t	NJetsISR;	# patJetsAK4PFCHSPt10NoLep jets within dR<0.3 of a selected prunedGenParticles
Int_t	nJets_CUT;
Int_t	nJets;	# jetsLVec
UInt_t	looseJetID;	Flag per 2016 Recommendation
UInt_t	tightJetID;	Flag per 2016 Recommendation
UInt_t	tightlepvetoJetID;	Flag per 2016 Recommendation
UInt_t	looseJetID_NoLep;
UInt_t	tightJetID_NoLep;
UInt_t	tightlepvetoJetID_NoLep;

Collections

Type	Collection Name	Description
vector<float>	puppitau1;	tau1 from puppiJetsLVec jets
vector<float>	puppitau2;	tau2 from puppiJetsLVec jets
vector<float>	puppitau3;	tau3 from puppiJetsLVec jets
vector<float>	puppisoftDropMass;	softDropMass from puppiJetsLVec jets
vector<float>	puppiSubJetsBdisc;	Bdisc from puppiSubJetsLVec jets
vector<float>	recoJetsJecUncLepCleaned;
vector<float>	prodJetsNoLep_qgLikelihood;
vector<float>	prodJetsNoLep_qgPtD;
vector<float>	prodJetsNoLep_qgAxis2;
vector<float>	recoJetschargedHadronEnergyFractionLepCleaned;
vector<float>	recoJetsneutralEmEnergyFractionLepCleaned;
vector<float>	recoJetschargedEmEnergyFractionLepCleaned;
vector<float>	recoJetsmuonEnergyFractionLepCleaned;
vector<float>	recoJetsBtag_0_LepCleaned;
vector<float>	recoJetsCharge_0_LepCleaned;
vector<float>	recoJetsJecScaleRawToFull_LepCleaned;
vector<float>	prodJetsNoLep_puppisoftDropMass;	soft drop mass of jets in No Lepton collection
vector<float>	prodJetsNoLep_puppitau1;	tau1 of jets in No Lepton collection
vector<float>	prodJetsNoLep_puppitau2;	tau1 of jets in No Lepton collection
vector<float>	prodJetsNoLep_puppitau3;	tau1 of jets in No Lepton collection
vector<float>	prodJetsNoLep_puppiSubJetsBdisc;	subjet B disc. of jets in No Lepton collection
vector<TLorentzVector>	jetsLVec	LVec of all AK4 jet collection*
vector<TLorentzVector>	puppiJetsLVec	all selectedPatJetsAK8PFPuppi jets
vector<TLorentzVector>	puppiSubJetsLVec;*	all daughter jets in selectedPatJetsAK8PFPuppiSoftDropPacked jets
vector<TLorentzVector>	jetsLVecLepCleaned;
vector<TLorentzVector>	prodJetsNoLep_puppiJetsLVec;	LVec of all jets in No Lepton collection
vector<TLorentzVector>	prodJetsNoLep_puppiSubJetsLVec;	LVec of subjets of all jets in No Lepton collection

Jet collections from miniAOD

https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetAnalysis

MiniAOD Analysis Documentation - 2017 VERSION UNDER CONSTRUCTION TWiki> CMSPublic Web> SWGuide> WorkBook > WorkBookMiniAOD > WorkBookMiniAOD2017

High level physics objects in miniAOD are saved in the PAT dataformats, which in turn derive from the RECO/AOD formats, so almost everything that you can find in the WorkBook documentation on PAT and physics objects applies to them direcly.

Jet Label	C++ Class	Selection	Detailed Information
slimmedJets	std::vector<pat::Jet>	ak4PFJetsCHS with pT > 10 GeV	L1+L2+L3+residual corrections are applied; b-tagging and pileup jet id information are embedded. Links are provided to the constituent PF candidates.
slimmedJetsPuppi	std::vector<pat::Jet>	puppi jets with pT > 20 GeV	L1+L2+L3+residual corrections are applied; b-tagging information is embedded. Links are provided to the constituent PF candidates.
slimmedGenJets	std::vector<reco::GenJet>	ak4GenJetsNoNu with pT > 8 GeV	links to constituents (packed gen particles) are available
slimmedJetsAK8	std::vector<pat::Jet>	ak8PFJetsPUPPI with pT > 100 GeV (for substructure)	L1+L2+L3+residual corrections are applied (AK8PFPuppi corrections used); some precomputed substructure info is provided, and also links to the constituent PF candidates. Subjets themselves are also stored. PF and substructure information is only stored for pT > 170 GeV. For lower pT studies, 3 jets are kept (with minimal information) from 30-100 GeV.
slimmedJetsAK8PFPuppiSoftDropPacked	std::vector<pat::Jet>	Subjets of PUPPI soft drop algorithm	L1+L2+L3+residual corrections are applied (AK4PFpuppi corrections used); b-tagging information is embedded. Links are provided to the constituent PF candidates.
slimmedGenJetsAK8	std::vector<reco::GenJet>	ak8GenJetsNoNu with pT > 150 GeV. For lower pT studies, 3 jets are kept (with minimal information) from 30-100 GeV.	links to constituents (packed gen particles) are available

Jet ID Recommendations

Recommendations for the 13 TeV data analysis, Run2016 (80X)

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NEW: For Run2017 see here. For the 13 TeV analysis for promt and rereco data of Run2016 the same recommendations as before are valid for |eta|<2.7 and 3<|eta|<5 (see table below).

For 2.7< |eta|<3 the JetID criteria have been changed (see table below) in order to increase the efficiency and background rejection as well. For more details look here

*For |eta|<2.7 Loose and Tight criteria are identical as before.

The JetID efficiencies for each different run (B-G) estimated and found consistent. For more details look here

Note that the jet ID requirements for |eta|>2.7 are not recommended for PUPPI jets (see e.g. this presentation

For -2.7 <= eta <= 2.7
PF Jet ID	Loose	Tight	TightLepVeto
Neutral Hadron Fraction	< 0.99	< 0.90	< 0.90
Neutral EM Fraction	< 0.99	< 0.90	< 0.90
Number of Constituents	> 1	> 1	> 1
Muon Fraction	-	-	< 0.8
And for -2.4 <= eta <= 2.4 in addition apply
Charged Hadron Fraction	> 0	> 0	> 0
Charged Multiplicity	> 0	> 0	> 0
Charged EM Fraction	< 0.99	< 0.99	< 0.90
For 2.7 < abs(eta) <= 3.0
PF Jet ID	Loose	Tight
Neutral EM Fraction	> 0.01	> 0.01
Neutral Hadron Fraction	< 0.98	< 0.98
Number of Neutral Particles	> 2	>2
For abs(eta) > 3.0
PF Jet ID	Loose	Tight
Neutral EM Fraction	< 0.90	< 0.90
Number of Neutral Particles	> 10	>10

For |eta|<=2.7 Apply

• looseJetID = (NHF<0.99 && NEMF<0.99 && NumConst >1) && ((abs(eta)<=2.4 && CHF>0 && CHM>0 && CEMF<0.99) || abs(eta)>2.4) && abs(eta)<=2.7
• tightJetID = (NHF<0.90 && NEMF<0.90 && NumConst >1) && ((abs(eta)<=2.4 && CHF>0 && CHM>0 && CEMF<0.99) || abs(eta)>2.4) && abs(eta)<=2.7 t
• tightLepVetoJetID = (NHF<0.90 && NEMF<0.90 && NumConst >1 && MUF<0.8) && ((abs(eta)<=2.4 && CHF>0 && CHM>0 && CEMF<0.90) || abs(eta)>2.4) && abs(eta)<=2.7
  For 2.7<|eta|<= 3.0 Apply

• looseJetID = (NHF<0.98 && NEMF>0.01 && NumNeutralParticle >2 && abs(eta)>2.7 && abs(eta)<=3.0 )
• tightJetID = (NHF<0.98 && NEMF>0.01 && NumNeutralParticle >2 && abs(eta)>2.7 && abs(eta)<=3.0 )
  For |eta|> 3.0 Apply

• looseJetID = (NEMF<0.90 && NumNeutralParticle >10 && abs(eta)>3.0 ) tightJetID = (NEMF<0.90 && NumNeutralParticle >10 && abs(eta)>3.0
  Note: All fractions are calculated with the raw/uncorrected energy of the jet (only then they add up to unity). So the PF JetID has to be applied before the jet energy corrections. To do this on a MINIAOD jet, you can get them as:

• NHF = pfjet->neutralHadronEnergyFraction();
• NEMF = pfjet->neutralEmEnergyFraction();
• CHF = pfjet->chargedHadronEnergyFraction();
• MUF = pfjet->muonEnergyFraction();
• CEMF = pfjet->chargedEmEnergyFraction();
• NumConst = pfjet->chargedMultiplicity()+pfjet->neutralMultiplicity();
• NumNeutralParticles =pfjet->neutralMultiplicity();
• CHM = pfjet->chargedMultiplicity();
  OR you can refine them (not reccomended):

• auto CHF = jet.chargedHadronEnergy() / jet.correctedP4(0).E() ;
• auto NHF = jet.neutralHadronEnergy() / jet.correctedP4(0).E() ;
• auto CM = jet.chargedMultiplicity() ; auto NM = jet.neutralMultiplicity() ;
• auto NEF = jet.neutralEmEnergy() / jet.correctedP4(0).E() ;
• auto CEF = jet.chargedEmEnergy() / jet.correctedP4(0).E() ;
• auto MUF = jet.muonEnergy() / jet.correctedP4(0).E() ;
• auto NumConst = jet.numberOfDaughters() ;

Recommendations for the 13 TeV data analysis, Run2017 (94X) Re-Reco

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In the following table, the preliminary JetID criteria for AK4CHS and PUPPI jets are listed. These are valid for all runs from B-F. Their efficiency is more than 98-99% for all eta regions, whereas their background rejection is more than 98% for |eta|<3. For more details see here. The criteria have been modified in order to achieve an efficiency of > 98-99% with the higher possible background rejection. For more details, see AN-18-093. Since the tight JetID efficiency is > 99% everywhere, loose is not recommended anymore.

For -2.7 <= eta <= 2.7, for AK4CHS and PUPPI
PF Jet ID	Tight	TightLepVeto
Neutral Hadron Fraction	< 0.90	< 0.90
Neutral EM Fraction	< 0.90	< 0.90
Number of Constituents	> 1	> 1
Muon Fraction	-	< 0.80
And for -2.4 <= eta <= 2.4 in addition apply
Charged Hadron Fraction	> 0	> 0
Charged Multiplicity	> 0	> 0
Charged EM Fraction	-	< 0.80
For 2.7 < abs(eta) <= 3.0 for AK4CHS
PF Jet ID	Tight
Neutral EM Fraction	> 0.02 and <0.99
Number of Neutral Particles	>2
For abs(eta) > 3.0 for AK4CHS
PF Jet ID	Tight
Neutral EM Fraction	< 0.90
Neutral Hadron Fraction	> 0.02
Number of Neutral Particles	>10
For 2.7 < abs(eta) <= 3.0 for PUPPI
PF Jet ID	Tight
Neutral Hadron Fraction	<0.99
For abs(eta) > 3.0 for PUPPI
PF Jet ID	Tight
Neutral EM Fraction	< 0.90
Neutral Hadron Fraction	> 0.02
Number of Neutral Particles	>2 and < 15

Note that for PUPPI jets, weighted multiplicties (of type float) were used which are available starting from CMSSW_9_4_6:

   jet->userFloat("patPuppiJetSpecificProducer:puppiMultiplicity") 
   jet->userFloat("patPuppiJetSpecificProducer:neutralPuppiMultiplicity")

The weighted multiplicties are only stored in MiniAOD samples produced with this release or higher by default. However, they can be computed on the fly as in this example.

A selector module for CMSSW is available in PFJetIDSelectionFuncto.

It can be used with such a code sniplet:

  from PhysicsTools.SelectorUtils.pfJetIDSelector_cfi import pfJetIDSelector
  process.goodPatJetsPFlow = cms.EDFilter(    "PFJetIDSelectionFunctorFilter", 
                                                 filterParams = pfJetIDSelector.clone(), 
                                                 src = cms.InputTag("selectedPatJetsPFlow"), 
                                                 filter = cms.bool(True))

Preliminary Recommendations for the 13 TeV data analysis, Run2018 (102X) Re-Reco

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In the following table, the preliminary JetID criteria for AK4CHS jets are listed. These are valid for all runs from A-D.

Their efficiency is more than 98-99% for all eta regions, whereas their background rejection is more than 98% for |eta| 98-99% with the higher possible background rejection.

We propose the JetIDLepVeto criteria for all analyses, except the ones that perform special cleaning or selections between jets and leptons in which, in order to avoid any kind of biases, we propose JetID without the vetoing on leptons.

For -2.6 <= eta <= 2.6, for AK4CHS
PF Jet ID	JetID	JetIDLepVeto
Neutral Hadron Fraction	< 0.90	< 0.90
Neutral EM Fraction	< 0.90	< 0.90
Number of Constituents	> 1	> 1
Muon Fraction	-	< 0.80
Charged Hadron Fraction	> 0	> 0
Charged Multiplicity	> 0	> 0
Charged EM Fraction	-	< 0.80
For 2.6 < abs(eta) <= 2.7, for AK4CHS
PF Jet ID	JetID	JetIDLepVeto
Neutral Hadron Fraction	< 0.90	< 0.90
Neutral EM Fraction	< 0.99	< 0.99
Muon Fraction	-	< 0.80
Charged Multiplicity	> 0	> 0
Charged EM Fraction	-	< 0.80
For 2.7 < abs(eta) <= 3.0 for AK4CHS
PF Jet ID	JetID
Neutral EM Fraction	> 0.02 and <0.99
Number of Neutral Particles	>2
For abs(eta) > 3.0 for AK4CHS
PF Jet ID	JetID
Neutral EM Fraction	< 0.90
Neutral Hadron Fraction	> 0.02
Number of Neutral Particles	>10

For |eta|<=2.6 Apply

• tightJetID = (abs(eta)<=2.6 && CEMF<0.8 && CHM>0 && CHF>0 && NumConst >1 && NEMF<0.9 && MUF <0.8 && NHF < 0.9 );
  For 2.6<|eta|<=2.7 Apply

• tightJetID = ( abs(eta)>2.6 && abs(eta)<=2.7 && CEMF<0.8 && CHM>0 && NEMF<0.99 && MUF <0.8 && NHF < 0.9 );
  For 2.7<|eta|<= 3.0 Apply

• tightJetID = ( NEMF>0.02 && NEMFNumNeutralParticle >2 && abs(eta)>2.7 && abs(eta)<=3.0 )
  For |eta|> 3.0 Apply

• tightJetID = (NEMF0.2 && NumNeutralParticle >10 && abs(eta)>3.0 )
  A selector module for CMSSW is available in PFJetIDSelectionFunctor . It can be used with such a code snippet:

from PhysicsTools.SelectorUtils.pfJetIDSelector_cfi import pfJetIDSelector
process.goodPatJetsPFlow = cms.EDFilter("PFJetIDSelectionFunctorFilter",
                                                filterParams = pfJetIDSelector.clone(),
                                                src = cms.InputTag("selectedPatJetsPFlow"),
                                                filter = cms.bool(True) )

Jet Energy Resolution

Recommendations for Data Analysis

Measurements show that the jet energy resolution (JER) in data is worse than in the simulation and the jets in MC need to be smeared to describe the data.

Smearing procedures

JER Scaling factors and Uncertainty for 13 TeV

Mandatory (JEC) Jet Energy Corrections at CMS.

The minimum correction levels to be applied on any CMS analysis using Monte Carlo and Data are:

Monte Carlo	L1 + L2L3 MC-truth
Data	L1 + L2L3 MC-truth + L2L3Residuals

Any analysis might place higher correction levels if necessary and available. Software instructions for applying these corrections can be found here.

Recommended Jet Energy Corrections and Uncertainties For Data and MC

This table should be used to find the recommended JEC correction set to be applied to your particular sample of DATA or MC for the given era and for each jet-type that is used. Note that the release era of the Data/MC is not necessarily the release version that you use to analyze your samples. The cells in the columns DATA and MC link to the corresponding software instructions in the WorkBookJetEnergyCorrections. As we forsee new releases being added regularly we order these releases from newer to older. Other releases for which we derive no corrections are not included. It is important to note that the Global Tags to be used need to be the ones appropriate for the CMSSW release used to analyze the MC or the data samples. If you use a different release your global tag might be wrong.

>>>>>>>>>>NOTE: We used Fall17_17Nov2017_V8_MC.db for Spring16 fastsim : Spring16 JEC produced errors

Recommended for MC

Recommended for Data

2017 Data

2016 Data

CMSSW 7.6.4 and above

Starting from CMSSW_7_6_4, the updateJetCollection function was added to PhysicsTools /PatAlgos/python/tools/jetTools.py which allows easy re-correction of pat::Jets as well as adding extra information to them. An example of its use can be found in PhysicsTools/PatAlgos/test/patTuple_updateJets_fromMiniAOD_cfg.py

The function is by default configured for pat::Jets stored in MiniAOD but can be used for any collection of pat::Jets with appropriately set input arguments.

If you just want to update the JEC in the MiniAOD, using the following python code is enough:

*from* PhysicsTools.PatAlgos.tools.jetTools *import* updateJetCollection

updateJetCollection(
        process,
        jetSource = cms.InputTag(*'slimmedJets'*)
        labelName = *'UpdatedJEC'*
        jetCorrections = (*'AK4PFchs'*, cms.vstring([<b>'L1FastJet'</b>, *'L2Relative'*, *'L3Absolute'*, <b>'L2L3Residual'</b>]), *'None'*) 
        _# Update: Safe to always add 'L2L3Residual' as MC contains dummy L2L3Residual corrections (always set to 1)_ )

The new jets collection, with the updated JEC, is updatedPatJetsUpdatedJEC, where by default no selection is applied (as in the standard PAT jet workflow). More generally, the final updated jet collection will be called updatedPatJets+labelName+postfix, with labelName and postfix set by default to an empty string.

If you are not using unscheduled mode (you really should using it), you need to add the following modules to your execution path (can be different if you change the labelName):

process.jecSequence = cms.Sequence(process.patJetCorrFactorsUpdatedJEC * process.updatedPatJetsUpdatedJEC)

Accessing the JEC uncertainties from the data base, directly in an EDAnalyzer:

edm::ESHandle<JetCorrectorParametersCollection> JetCorParColl;
iSetup.get<JetCorrectionsRecord>().get("AK5PF",JetCorParColl);
JetCorrectorParameters const & JetCorPar = (*JetCorParColl)["Uncertainty"];
JetCorrectionUncertainty *jecUnc = new JetCorrectionUncertainty (JetCorPar);

Links

• JetMet Indico: https://indico.cern.ch/category/1308/
• JetMET Physics Object Group (JME POG): https://twiki.cern.ch/twiki/bin/view/CMS/JetMET
• Jet Energy Resolution: https://twiki.cern.ch/twiki/bin/view/CMS/JetResolution
• JetMET Algorithms and Reconstruction (JMAR) Group: https://twiki.cern.ch/twiki/bin/view/CMS/JetMETAlgorithmsReconstruction
• SUSY recommendations: https://twiki.cern.ch/twiki/bin/view/CMS/SUSRecommendations18
• 4.7 MiniAOD Analysis Documentation: https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookMiniAOD2017

-- JackWKingIII - 2019-01-24