NanoAOD/python/taus_cff.py

0001 import FWCore.ParameterSet.Config as cms
0002 from PhysicsTools.NanoAOD.common_cff import *
0003 from PhysicsTools.NanoAOD.simpleCandidateFlatTableProducer_cfi import simpleCandidateFlatTableProducer
0004 from PhysicsTools.NanoAOD.simpleGenParticleFlatTableProducer_cfi import simpleGenParticleFlatTableProducer
0005 from PhysicsTools.NanoAOD.simplePATTauFlatTableProducer_cfi import simplePATTauFlatTableProducer
0006
0007 from PhysicsTools.JetMCAlgos.TauGenJets_cfi import tauGenJets
0008 from PhysicsTools.JetMCAlgos.TauGenJetsDecayModeSelectorAllHadrons_cfi import tauGenJetsSelectorAllHadrons
0009
0010 from PhysicsTools.PatAlgos.patTauSignalCandidatesProducer_cfi import patTauSignalCandidatesProducer
0011
0012 ##################### User floats producers, selectors ##########################
0013
0014 # Original DeepTau v2p5 in 12_4_X doesn't include WPs in MINIAOD
0015 # Import thresholds here to define WPs manually from raw scores
0016 from RecoTauTag.RecoTau.tauIdWPsDefs import WORKING_POINTS_v2p5
0017
0018 finalTaus = cms.EDFilter("PATTauRefSelector",
0019     src = cms.InputTag("slimmedTaus"),
0020     cut = cms.string("pt > 18 && ((tauID('decayModeFindingNewDMs') > 0.5 && (tauID('byLooseCombinedIsolationDeltaBetaCorr3Hits') || (tauID('chargedIsoPtSumdR03')+max(0.,tauID('neutralIsoPtSumdR03')-0.072*tauID('puCorrPtSum'))<2.5) || tauID('byVVVLooseDeepTau2018v2p5VSjet'))) || (?isTauIDAvailable('byUTagCHSVSjetraw')?tauID('byUTagCHSVSjetraw'):-1) > {} || (?isTauIDAvailable('byUTagPUPPIVSjetraw')?tauID('byUTagPUPPIVSjetraw'):-1) > {})".format(0.05, 0.05))
0021 )
0022
0023 ##################### Tables for final output and docs ##########################
0024 def _tauIdWPMask(pattern, choices, doc="", from_raw=False, wp_thrs=None):
0025     if from_raw:
0026         assert wp_thrs is not None, "wp_thrs argument in _tauIdWPMask() is None, expect it to be dict-like"
0027
0028         var_definition = []
0029         for wp_name in choices:
0030             if not isinstance(wp_thrs[wp_name], float):
0031                 raise TypeError("Threshold for WP=%s is not a float number." % wp_name)
0032             wp_definition = "test_bit(tauID('{}')-{}+1,0)".format(pattern, wp_thrs[wp_name])
0033             var_definition.append(wp_definition)
0034         var_definition = " + ".join(var_definition)
0035         var_definition = ("?isTauIDAvailable('%s')?(" % pattern) + var_definition + "):0"
0036     else:
0037         var_definition = " + ".join(["tauID('%s')" % (pattern % c) for c in choices])
0038         var_definition = ("?isTauIDAvailable('%s')?(" % (pattern % choices[0])) + var_definition + "):0"
0039
0040     doc = doc + ": "+", ".join(["%d = %s" % (i,c) for (i,c) in enumerate(choices, start=1)])
0041     return Var(var_definition, "uint8", doc=doc)
0042
0043
0044 tauTable = simplePATTauFlatTableProducer.clone(
0045     src = cms.InputTag("linkedObjects","taus"),
0046     name= cms.string("Tau"),
0047     doc = cms.string("slimmedTaus after basic selection (" + finalTaus.cut.value()+")")
0048 )
0049
0050 _tauVarsBase = cms.PSet(P4Vars,
0051        charge = Var("charge", "int16", doc="electric charge"),
0052        jetIdx = Var("?hasUserCand('jet')?userCand('jet').key():-1", "int16", doc="index of the associated jet (-1 if none)"),
0053        eleIdx = Var("?overlaps('electrons').size()>0?overlaps('electrons')[0].key():-1", "int16", doc="index of first matching electron"),
0054        muIdx = Var("?overlaps('muons').size()>0?overlaps('muons')[0].key():-1", "int16", doc="index of first matching muon"),
0055        svIdx1 = Var("?overlaps('vertices').size()>0?overlaps('vertices')[0].key():-1", "int16", doc="index of first matching secondary vertex"),
0056        svIdx2 = Var("?overlaps('vertices').size()>1?overlaps('vertices')[1].key():-1", "int16", doc="index of second matching secondary vertex"),
0057        nSVs = Var("?hasOverlaps('vertices')?overlaps('vertices').size():0", "uint8", doc="number of secondary vertices in the tau"),
0058        decayMode = Var("decayMode()", "uint8"),
0059        idDecayModeOldDMs = Var("(?isTauIDAvailable('decayModeFinding')?tauID('decayModeFinding'):-1) > 0", bool),
0060        idDecayModeNewDMs = Var("(?isTauIDAvailable('decayModeFindingNewDMs')?tauID('decayModeFindingNewDMs'):-1) > 0", bool),
0061        leadTkPtOverTauPt = Var("?leadChargedHadrCand.isNonnull()?leadChargedHadrCand.pt/pt:1",float, doc="pt of the leading track divided by tau pt",precision=10),
0062        leadTkDeltaEta = Var("?leadChargedHadrCand.isNonnull()?(leadChargedHadrCand.eta - eta):0",float, doc="eta of the leading track, minus tau eta",precision=8),
0063        leadTkDeltaPhi = Var("?leadChargedHadrCand.isNonnull()?deltaPhi(leadChargedHadrCand.phi, phi):0",float, doc="phi of the leading track, minus tau phi",precision=8),
0064
0065        # lazyEval=True: leadChargedHadrCand() returns the base type `reco::CandidatePtr`, needs to be dynamically casted to PackedCandidate to call dxy() / dz()
0066        dxy = Var("?leadChargedHadrCand.isNonnull()?leadChargedHadrCand().dxy():0",float, doc="d_{xy} of lead track with respect to PV, in cm (with sign)",precision=10, lazyEval=True),
0067        dz = Var("?leadChargedHadrCand.isNonnull()?leadChargedHadrCand().dz():0",float, doc="d_{z} of lead track with respect to PV, in cm (with sign)",precision=14, lazyEval=True),
0068
0069        # these are too many, we may have to suppress some
0070        rawIso = Var("?isTauIDAvailable('byCombinedIsolationDeltaBetaCorrRaw3Hits')?tauID('byCombinedIsolationDeltaBetaCorrRaw3Hits'):-1", float, doc = "combined isolation (deltaBeta corrections)", precision=10),
0071        rawIsodR03 = Var("?isTauIDAvailable('chargedIsoPtSumdR03')?(tauID('chargedIsoPtSumdR03')+max(0.,tauID('neutralIsoPtSumdR03')-0.072*tauID('puCorrPtSum'))):-1", float, doc = "combined isolation (deltaBeta corrections, dR=0.3)", precision=10),
0072        chargedIso = Var("?isTauIDAvailable('chargedIsoPtSum')?tauID('chargedIsoPtSum'):-1", float, doc = "charged isolation", precision=10),
0073        neutralIso = Var("?isTauIDAvailable('neutralIsoPtSum')?tauID('neutralIsoPtSum'):-1", float, doc = "neutral (photon) isolation", precision=10),
0074        puCorr = Var("?isTauIDAvailable('puCorrPtSum')?tauID('puCorrPtSum'):-1", float, doc = "pileup correction", precision=10),
0075        photonsOutsideSignalCone = Var("?isTauIDAvailable('photonPtSumOutsideSignalCone')?tauID('photonPtSumOutsideSignalCone'):-1", float, doc = "sum of photons outside signal cone", precision=10),
0076
0077        idAntiMu = _tauIdWPMask("againstMuon%s3", choices=("Loose","Tight"), doc= "Anti-muon discriminator V3: "),
0078        idAntiEleDeadECal = Var("(?isTauIDAvailable('againstElectronDeadECAL')?tauID('againstElectronDeadECAL'):-1) > 0", bool, doc = "Anti-electron dead-ECal discriminator"),
0079
0080 )
0081
0082 _deepTauVars2018v2p5 = cms.PSet(
0083     rawDeepTau2018v2p5VSe = Var("?isTauIDAvailable('byDeepTau2018v2p5VSeraw')?tauID('byDeepTau2018v2p5VSeraw'):-1", float, doc="byDeepTau2018v2p5VSe raw output discriminator (deepTau2018v2p5)", precision=10),
0084     rawDeepTau2018v2p5VSmu = Var("?isTauIDAvailable('byDeepTau2018v2p5VSmuraw')?tauID('byDeepTau2018v2p5VSmuraw'):-1", float, doc="byDeepTau2018v2p5VSmu raw output discriminator (deepTau2018v2p5)", precision=10),
0085     rawDeepTau2018v2p5VSjet = Var("?isTauIDAvailable('byDeepTau2018v2p5VSjetraw')?tauID('byDeepTau2018v2p5VSjetraw'):-1", float, doc="byDeepTau2018v2p5VSjet raw output discriminator (deepTau2018v2p5)", precision=10),
0086     idDeepTau2018v2p5VSe = _tauIdWPMask("by%sDeepTau2018v2p5VSe",
0087                                             choices=("VVVLoose","VVLoose","VLoose","Loose","Medium","Tight","VTight","VVTight"),
0088                                             doc="byDeepTau2018v2p5VSe ID working points (deepTau2018v2p5)"),
0089     idDeepTau2018v2p5VSmu = _tauIdWPMask("by%sDeepTau2018v2p5VSmu",
0090                                             choices=("VLoose", "Loose", "Medium", "Tight"),
0091                                             doc="byDeepTau2018v2p5VSmu ID working points (deepTau2018v2p5)"),
0092     idDeepTau2018v2p5VSjet = _tauIdWPMask("by%sDeepTau2018v2p5VSjet",
0093                                             choices=("VVVLoose","VVLoose","VLoose","Loose","Medium","Tight","VTight","VVTight"),
0094                                             doc="byDeepTau2018v2p5VSjet ID working points (deepTau2018v2p5)"),
0095 )
0096
0097 _UTagCHS = cms.PSet(
0098     decayModePNet = Var("?isTauIDAvailable('byUTagCHSDecayMode')?tauID('byUTagCHSDecayMode'):-1", "int16",doc="decay mode of the highest tau score of ParticleNet (CHS Jets)"),
0099     rawPNetVSe = Var("?isTauIDAvailable('byUTagCHSVSeraw')?tauID('byUTagCHSVSeraw'):-1", float, doc="raw output of ParticleNetVsE discriminator (PNet 2023 - CHS Jets)", precision=10),
0100     rawPNetVSmu = Var("?isTauIDAvailable('byUTagCHSVSmuraw')?tauID('byUTagCHSVSmuraw'):-1", float, doc="raw output of ParticleNetVsMu discriminator (PNet 2023 - CHS Jets)", precision=10),
0101     rawPNetVSjet = Var("?isTauIDAvailable('byUTagCHSVSjetraw')?tauID('byUTagCHSVSjetraw'):-1", float, doc="raw output of ParticleNetVsJet discriminator (PNet 2023 - CHS Jets)", precision=10),
0102     ptCorrPNet = Var("?isTauIDAvailable('byUTagCHSPtCorr')?tauID('byUTagCHSPtCorr'):1", float, doc="pt correction (PNet 2023 - CHS Jets)", precision=10),
0103     qConfPNet = Var("?isTauIDAvailable('byUTagCHSQConf')?tauID('byUTagCHSQConf'):0", float, doc="signed charge confidence (PNet 2023 - CHS Jets)", precision=10),
0104     probDM0PNet = Var("?isTauIDAvailable('byUTagCHSProb1h0pi0')?tauID('byUTagCHSProb1h0pi0'):-1", float, doc="normalised probablity of decayMode 0, 1h+0pi0 (PNet 2023 - CHS Jets)", precision=10),
0105     probDM1PNet = Var("?isTauIDAvailable('byUTagCHSProb1h1pi0')?tauID('byUTagCHSProb1h1pi0'):-1", float, doc="normalised probablity of decayMode 1, 1h+1pi0 (PNet 2023 - CHS Jets)", precision=10),
0106     probDM2PNet = Var("?isTauIDAvailable('byUTagCHSProb1h2pi0')?tauID('byUTagCHSProb1h2pi0'):-1", float, doc="normalised probablity of decayMode 2, 1h+2pi0 (PNet 2023 - CHS Jets)", precision=10),
0107     probDM10PNet = Var("?isTauIDAvailable('byUTagCHSProb3h0pi0')?tauID('byUTagCHSProb3h0pi0'):-1", float, doc="normalised probablity of decayMode 10, 3h+0pi0 (PNet 2023 - CHS Jets)", precision=10),
0108     probDM11PNet = Var("?isTauIDAvailable('byUTagCHSProb3h1pi0')?tauID('byUTagCHSProb3h1pi0'):-1", float, doc="normalised probablity of decayMode 11, 3h+1pi0 (PNet 2023 - CHS Jets)", precision=10),
0109 )
0110
0111 _UTagPUPPI = cms.PSet(
0112     decayModeUParT= Var("?isTauIDAvailable('byUTagPUPPIDecayMode')?tauID('byUTagPUPPIDecayMode'):-1", "int16",doc="decay mode of the highest tau score of Unified ParT 2024 (PUPPI Jets)"),
0113     rawUParTVSe = Var("?isTauIDAvailable('byUTagPUPPIVSeraw')?tauID('byUTagPUPPIVSeraw'):-1", float, doc="raw output of UParTVsE discriminator (Unified ParT 2024 - PUPPI Jets)", precision=10),
0114     rawUParTVSmu = Var("?isTauIDAvailable('byUTagPUPPIVSmuraw')?tauID('byUTagPUPPIVSmuraw'):-1", float, doc="raw output of UParTVsMu discriminator (Unified ParT 2024 - PUPPI Jets)", precision=10),
0115     rawUParTVSjet = Var("?isTauIDAvailable('byUTagPUPPIVSjetraw')?tauID('byUTagPUPPIVSjetraw'):-1", float, doc="raw output of UParTVsJet discriminator (Unified ParT 2024 - PUPPI Jets)", precision=10),
0116     ptCorrUParT= Var("?isTauIDAvailable('byUTagPUPPIPtCorr')?tauID('byUTagPUPPIPtCorr'):1", float, doc="pt correction (Unified ParT 2024 - PUPPI Jets)", precision=10),
0117     qConfUParT= Var("?isTauIDAvailable('byUTagPUPPIQConf')?tauID('byUTagPUPPIQConf'):0", float, doc="signed charge confidence (Unified ParT 2024 - PUPPI Jets)", precision=10),
0118     probDM0UParT= Var("?isTauIDAvailable('byUTagPUPPIProb1h0pi0')?tauID('byUTagPUPPIProb1h0pi0'):-1", float, doc="normalised probablity of decayMode 0, 1h+0pi0 (Unified ParT 2024 - PUPPI Jets)", precision=10),
0119     probDM1UParT= Var("?isTauIDAvailable('byUTagPUPPIProb1h1pi0')?tauID('byUTagPUPPIProb1h1pi0'):-1", float, doc="normalised probablity of decayMode 1, 1h+1pi0 (Unified ParT 2024 - PUPPI Jets)", precision=10),
0120     probDM2UParT= Var("?isTauIDAvailable('byUTagPUPPIProb1h2pi0')?tauID('byUTagPUPPIProb1h2pi0'):-1", float, doc="normalised probablity of decayMode 2, 1h+2pi0 (Unified ParT 2024 - PUPPI Jets)", precision=10),
0121     probDM10UParT= Var("?isTauIDAvailable('byUTagPUPPIProb3h0pi0')?tauID('byUTagPUPPIProb3h0pi0'):-1", float, doc="normalised probablity of decayMode 10, 3h+0pi0 (Unified ParT 2024 - PUPPI Jets)", precision=10),
0122     probDM11UParT= Var("?isTauIDAvailable('byUTagPUPPIProb3h1pi0')?tauID('byUTagPUPPIProb3h1pi0'):-1", float, doc="normalised probablity of decayMode 11, 3h+1pi0 (Unified ParT 2024 - PUPPI Jets)", precision=10),
0123 )
0124
0125 _variablesMiniV2 = cms.PSet(
0126     _tauVarsBase,
0127     _deepTauVars2018v2p5,
0128     _UTagCHS,
0129     _UTagPUPPI
0130 )
0131
0132 tauTable.variables = _variablesMiniV2
0133
0134 tauSignalCands = patTauSignalCandidatesProducer.clone(
0135     src = tauTable.src,
0136     storeLostTracks = True
0137 )
0138
0139 tauSignalCandsTable = simpleCandidateFlatTableProducer.clone(
0140     src = cms.InputTag("tauSignalCands"),
0141     cut = cms.string("pt > 0."),
0142     name = cms.string("TauProd"),
0143     doc = cms.string("tau signal candidates"),
0144     variables = cms.PSet(
0145         P3Vars,
0146         pdgId = Var("pdgId", int, doc="PDG code assigned by the event reconstruction (not by MC truth)"),
0147         tauIdx = Var("status", "int16", doc="index of the mother tau"),
0148         #trkPt = Var("?daughter(0).hasTrackDetails()?daughter(0).bestTrack().pt():0", float, precision=-1, doc="pt of associated track"), #MB: better to store ratio over cand pt?
0149     )
0150 )
0151
0152 tauGenJetsForNano = tauGenJets.clone(
0153     GenParticles = "finalGenParticles",
0154     includeNeutrinos = False
0155 )
0156
0157 tauGenJetsSelectorAllHadronsForNano = tauGenJetsSelectorAllHadrons.clone(
0158     src = "tauGenJetsForNano"
0159 )
0160
0161 genVisTaus = cms.EDProducer("GenVisTauProducer",
0162     src = cms.InputTag("tauGenJetsSelectorAllHadronsForNano"),
0163     srcGenParticles = cms.InputTag("finalGenParticles")
0164 )
0165
0166 genVisTauTable = simpleGenParticleFlatTableProducer.clone(
0167     src = cms.InputTag("genVisTaus"),
0168     cut = cms.string("pt > 10."),
0169     name = cms.string("GenVisTau"),
0170     doc = cms.string("gen hadronic taus "),
0171     variables = cms.PSet(
0172          pt = Var("pt", float,precision=8),
0173          phi = Var("phi", float,precision=8),
0174          eta = Var("eta", float,precision=8),
0175          mass = Var("mass", float,precision=8),
0176      charge = Var("charge", "int16"),
0177      status = Var("status", "uint8", doc="Hadronic tau decay mode. 0=OneProng0PiZero, 1=OneProng1PiZero, 2=OneProng2PiZero, 10=ThreeProng0PiZero, 11=ThreeProng1PiZero, 15=Other"),
0178      genPartIdxMother = Var("?numberOfMothers>0?motherRef(0).key():-1", "int16", doc="index of the mother particle"),
0179     )
0180 )
0181
0182 tausMCMatchLepTauForTable = cms.EDProducer("MCMatcher",  # cut on deltaR, deltaPt/Pt; pick best by deltaR
0183     src         = tauTable.src,                 # final reco collection
0184     matched     = cms.InputTag("finalGenParticles"), # final mc-truth particle collection
0185     mcPdgId     = cms.vint32(11,13),            # one or more PDG ID (11 = electron, 13 = muon); absolute values (see below)
0186     checkCharge = cms.bool(False),              # True = require RECO and MC objects to have the same charge
0187     mcStatus    = cms.vint32(),                 # PYTHIA status code (1 = stable, 2 = shower, 3 = hard scattering)
0188     maxDeltaR   = cms.double(0.3),              # Minimum deltaR for the match
0189     maxDPtRel   = cms.double(0.5),              # Minimum deltaPt/Pt for the match
0190     resolveAmbiguities    = cms.bool(True),     # Forbid two RECO objects to match to the same GEN object
0191     resolveByMatchQuality = cms.bool(True),     # False = just match input in order; True = pick lowest deltaR pair first
0192 )
0193
0194 tausMCMatchHadTauForTable = cms.EDProducer("MCMatcher",  # cut on deltaR, deltaPt/Pt; pick best by deltaR
0195     src         = tauTable.src,                 # final reco collection
0196     matched     = cms.InputTag("genVisTaus"),   # generator level hadronic tau decays
0197     mcPdgId     = cms.vint32(15),               # one or more PDG ID (15 = tau); absolute values (see below)
0198     checkCharge = cms.bool(False),              # True = require RECO and MC objects to have the same charge
0199     mcStatus    = cms.vint32(),                 # CV: no *not* require certain status code for matching (status code corresponds to decay mode for hadronic tau decays)
0200     maxDeltaR   = cms.double(0.3),              # Maximum deltaR for the match
0201     maxDPtRel   = cms.double(1.),               # Maximum deltaPt/Pt for the match
0202     resolveAmbiguities    = cms.bool(True),     # Forbid two RECO objects to match to the same GEN object
0203     resolveByMatchQuality = cms.bool(True),     # False = just match input in order; True = pick lowest deltaR pair first
0204 )
0205
0206 tauMCTable = cms.EDProducer("CandMCMatchTableProducer",
0207     src = tauTable.src,
0208     mcMap = cms.InputTag("tausMCMatchLepTauForTable"),
0209     mcMapVisTau = cms.InputTag("tausMCMatchHadTauForTable"),
0210     objName = tauTable.name,
0211     objType = tauTable.name, #cms.string("Tau"),
0212     branchName = cms.string("genPart"),
0213     docString = cms.string("MC matching to status==2 taus"),
0214 )
0215
0216
0217 tauTask = cms.Task(finalTaus)
0218 tauTablesTask = cms.Task(tauTable)
0219 tauSignalCandsTask = cms.Task(tauSignalCands,tauSignalCandsTable)
0220 tauTablesTask.add(tauSignalCandsTask)
0221
0222 genTauTask = cms.Task(tauGenJetsForNano,tauGenJetsSelectorAllHadronsForNano,genVisTaus,genVisTauTable)
0223 tauMCTask = cms.Task(genTauTask,tausMCMatchLepTauForTable,tausMCMatchHadTauForTable,tauMCTable)
0224