def splitTTjetFlavours(cfg, tree, noSel):
subProc = cfg["subprocess"]
if subProc == "ttbb":
noSel = noSel.refine(subProc, cut=(tree.genTtbarId % 100) >= 52)
elif subProc == "ttbj":
noSel = noSel.refine(subProc, cut=(tree.genTtbarId % 100) == 51)
elif subProc == "ttcc":
noSel = noSel.refine(subProc,
cut=op.in_range(40, tree.genTtbarId % 100, 46))
elif subProc == "ttjj":
noSel = noSel.refine(subProc, cut=(tree.genTtbarId % 100) < 41)
return noSel
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
metSel = noSel.refine("MET", cut=(tree.met.et > 30000))
trigSel = metSel.refine("trig", cut=op.OR(tree.trigE, tree.trigM))
goodLeptons = op.select(
tree.lep, lambda l: op.AND(l.isTightID, l.pt > 35000., l.ptcone30 /
l.pt < 0.1, l.etcone20 / l.pt < 0.1))
oneLepSel = trigSel.refine("1goodlep",
cut=(op.rng_len(goodLeptons) == 1))
lep = goodLeptons[0]
signalSel = oneLepSel.refine(
"signalRegion",
cut=op.AND(
op.abs(lep.z0 * op.sin(lep.p4.theta())) < 0.5,
op.multiSwitch(
(lep.type == 11,
op.AND(
op.abs(lep.eta) < 2.46,
op.NOT(op.in_range(1.37, op.abs(lep.eta), 1.52)),
op.abs(lep.trackd0pvunbiased /
lep.tracksigd0pvunbiased) < 5)),
(lep.type == 13,
op.AND(
op.abs(lep.eta) < 2.5,
op.abs(lep.trackd0pvunbiased /
lep.tracksigd0pvunbiased) < 3)),
op.c_bool(False))))
metp4 = makePtEtaPhiEP4(tree.met.et, op.c_float(0.), tree.met.phi,
tree.met.et)
plots.append(
Plot.make1D("mt_w", (lep.p4 + metp4).Mt() / 1000.,
signalSel,
EqBin(40, 60., 180.),
title="m_{T}^{W#rightarrow l#nu} (GeV)",
plotopts={"log-y-axis-range": [25., 2.5e8]}))
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
dimu_Z = op.combine(
tree.Muon,
N=2,
pred=(lambda mu1, mu2: op.AND(
mu1.charge != mu2.charge,
op.in_range(60., op.invariant_mass(mu1.p4, mu2.p4), 120.))))
hasDiMuZ = noSel.refine("hasDiMuZ", cut=(op.rng_len(dimu_Z) > 0))
plots.append(
Plot.make1D("dimuZ_MET",
tree.MET.pt,
hasDiMuZ,
EqBin(100, 0., 2000.),
title="MET (GeV)"))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
# Some imports #
from bamboo.analysisutils import forceDefine
era = sampleCfg['era']
# Get pile-up configs #
puWeightsFile = None
if era == "2016":
sfTag = "94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2016.json")
elif era == "2017":
sfTag = "94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2017.json")
elif era == "2018":
sfTag = "102X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2018.json")
if self.isMC(sample) and puWeightsFile is not None:
from bamboo.analysisutils import makePileupWeight
noSel = noSel.refine("puWeight",
weight=makePileupWeight(puWeightsFile,
t.Pileup_nTrueInt,
systName="pileup"))
isMC = self.isMC(sample)
plots = []
forceDefine(t._Muon.calcProd, noSel)
#############################################################################
################################ Muons #####################################
#############################################################################
# Wp // 2016- 2017 -2018 : Muon_mediumId // https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2#Muon_Isolation
muonsByPt = op.sort(t.Muon, lambda mu: -mu.p4.Pt())
muons = op.select(
muonsByPt, lambda mu: op.AND(mu.p4.Pt() > 10.,
op.abs(mu.p4.Eta()) < 2.4, mu.tightId,
mu.pfRelIso04_all < 0.15))
# Scalefactors #
#if era=="2016":
# doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
# muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), combine="weight", systName="muid")
# muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), combine="weight", systName="muiso")
# TrkIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "highpt"), combine="weight")
# TrkISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "isotrk_loose_idtrk_tightidandipcut"), combine="weight")
#else:
# muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), systName="muid")
# muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), systName="muiso")
#############################################################################
############################# Electrons ###################################
#############################################################################
#Wp // 2016: Electron_cutBased_Sum16==3 -> medium // 2017 -2018 : Electron_cutBased ==3 --> medium ( Fall17_V2)
# asking for electrons to be in the Barrel region with dz<1mm & dxy< 0.5mm // Endcap region dz<2mm & dxy< 0.5mm
electronsByPt = op.sort(t.Electron, lambda ele: -ele.p4.Pt())
electrons = op.select(
electronsByPt,
lambda ele: op.AND(ele.p4.Pt() > 15.,
op.abs(ele.p4.Eta()) < 2.5, ele.cutBased >= 3)
) # //cut-based ID Fall17 V2 the recomended one from POG for the FullRunII
# Scalefactors #
#elMediumIDSF = get_scalefactor("lepton", ("electron_{0}_{1}".format(era,sfTag), "id_medium"), systName="elid")
#doubleEleTrigSF = get_scalefactor("dilepton", ("doubleEleLeg_HHMoriond17_2016"), systName="eleltrig")
#elemuTrigSF = get_scalefactor("dilepton", ("elemuLeg_HHMoriond17_2016"), systName="elmutrig")
#mueleTrigSF = get_scalefactor("dilepton", ("mueleLeg_HHMoriond17_2016"), systName="mueltrig")
OsElEl = op.combine(
electrons,
N=2,
pred=lambda el1, el2: op.AND(el1.charge != el2.charge,
el1.p4.Pt() > 25,
el2.p4.Pt() > 15))
OsMuMu = op.combine(
muons,
N=2,
pred=lambda mu1, mu2: op.AND(mu1.charge != mu2.charge,
mu1.p4.Pt() > 25,
mu2.p4.Pt() > 15))
OsElMu = op.combine((electrons, muons),
pred=lambda el, mu: op.AND(el.charge != mu.charge,
el.p4.Pt() > 25,
mu.p4.Pt() > 15))
OsMuEl = op.combine((electrons, muons),
pred=lambda el, mu: op.AND(el.charge != mu.charge,
el.p4.Pt() > 15,
mu.p4.Pt() > 25))
hasOsElEl = noSel.refine("hasOsElEl", cut=[op.rng_len(OsElEl) >= 1])
hasOsMuMu = noSel.refine("hasOsMuMu", cut=[op.rng_len(OsMuMu) >= 1])
hasOsElMu = noSel.refine("hasOsElMu", cut=[op.rng_len(OsElMu) >= 1])
hasOsMuEl = noSel.refine("hasOsMuEl", cut=[op.rng_len(OsMuEl) >= 1])
plots.append(
Plot.make1D("ElEl_channel",
op.rng_len(OsElEl),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in ElEl channel',
xTitle='N_{dilepton} (ElEl channel)'))
plots.append(
Plot.make1D("MuMu_channel",
op.rng_len(OsMuMu),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in MuMu channel',
xTitle='N_{dilepton} (MuMu channel)'))
plots.append(
Plot.make1D("ElMu_channel",
op.rng_len(OsElMu),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in ElMu channel',
xTitle='N_{dilepton} (ElMu channel)'))
plots.append(
Plot.make1D("MuEl_channel",
op.rng_len(OsMuEl),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in MuEl channel',
xTitle='N_{dilepton} (MuEl channel)'))
plots += makeDileptonPlots(self,
sel=hasOsElEl,
dilepton=OsElEl[0],
suffix='hasOsdilep',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMu,
dilepton=OsMuMu[0],
suffix='hasOsdilep',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMu,
dilepton=OsElMu[0],
suffix='hasOsdilep',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuEl,
dilepton=OsMuEl[0],
suffix='hasOsdilep',
channel='MuEl')
# Dilepton Z peak exclusion (charge already done in previous selection) #
lambda_mllLowerband = lambda dilep: op.in_range(
12., op.invariant_mass(dilep[0].p4, dilep[1].p4), 80.)
lambda_mllUpperband = lambda dilep: op.invariant_mass(
dilep[0].p4, dilep[1].p4) > 100.
lambda_mllCut = lambda dilep: op.OR(lambda_mllLowerband(dilep),
lambda_mllUpperband(dilep))
hasOsElElOutZ = hasOsElEl.refine("hasOsElElOutZ",
cut=[lambda_mllCut(OsElEl[0])])
hasOsMuMuOutZ = hasOsMuMu.refine("hasOsMuMuOutZ",
cut=[lambda_mllCut(OsMuMu[0])])
hasOsElMuOutZ = hasOsElMu.refine("hasOsElMuOutZ",
cut=[lambda_mllCut(OsElMu[0])])
hasOsMuElOutZ = hasOsMuEl.refine("hasOsMuElOutZ",
cut=[lambda_mllCut(OsMuEl[0])])
plots += makeDileptonPlots(self,
sel=hasOsElElOutZ,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZ,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZ,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZ,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ',
channel='MuEl')
#############################################################################
################################ Jets #####################################
#############################################################################
# select jets // 2016 - 2017 - 2018 ( j.jetId &2) -> tight jet ID
jetsByPt = op.sort(t.Jet, lambda jet: -jet.p4.Pt())
jetsSel = op.select(jetsByPt,
lambda j: op.AND(j.p4.Pt() > 20.,
op.abs(j.p4.Eta()) < 2.4,
(j.jetId & 2))) # Jets = AK4 jets
fatjetsByPt = op.sort(t.FatJet, lambda fatjet: -fatjet.p4.Pt())
fatjetsSel = op.select(
fatjetsByPt, lambda j: op.AND(j.p4.Pt() > 20.,
op.abs(j.p4.Eta()) < 2.4,
(j.jetId & 2))) # FatJets = AK8 jets
# exclude from the jetsSel any jet that happens to include within its reconstruction cone a muon or an electron.
jets = op.select(
jetsSel, lambda j: op.AND(
op.NOT(
op.rng_any(electrons, lambda ele: op.deltaR(j.p4, ele.p4) <
0.3)),
op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(j.p4, mu.p4) < 0.3))
))
fatjets = op.select(
fatjetsSel, lambda j: op.AND(
op.NOT(
op.rng_any(electrons, lambda ele: op.deltaR(j.p4, ele.p4) <
0.3)),
op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(j.p4, mu.p4) < 0.3))
))
# Boosted and resolved jets categories #
if era == "2016": # Must check that subJet exists before looking at the btag
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.6321),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.6321))
lambda_resolved = lambda jet: jet.btagDeepB > 0.6321
elif era == "2017":
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.4941),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.4941))
lambda_resolved = lambda jet: jet.btagDeepB > 0.4941
elif era == "2018":
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.4184),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.4184))
lambda_resolved = lambda jet: jet.btagDeepB > 0.4184
# Select the bjets we want #
bjetsResolved = op.select(jets, lambda_resolved)
bjetsBoosted = op.select(fatjets, lambda_boosted)
# Define the boosted and Resolved (+exclusive) selections #
hasBoostedJets = noSel.refine("hasBoostedJets",
cut=[op.rng_len(bjetsBoosted) >= 1])
hasNotBoostedJets = noSel.refine("hasNotBoostedJets",
cut=[op.rng_len(bjetsBoosted) == 0])
hasResolvedJets = noSel.refine(
"hasResolvedJets",
cut=[op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1])
hasNotResolvedJets = noSel.refine(
"hasNotResolvedJets",
cut=[op.OR(op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)])
hasBoostedAndResolvedJets = noSel.refine(
"hasBoostedAndResolvedJets",
cut=[
op.rng_len(bjetsBoosted) >= 1,
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1
])
hasNotBoostedAndResolvedJets = noSel.refine(
"hasNotBoostedAndResolvedJets",
cut=[
op.OR(
op.rng_len(bjetsBoosted) == 0,
op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)
])
hasExlusiveResolvedJets = noSel.refine(
"hasExlusiveResolved",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasNotExlusiveResolvedJets = noSel.refine(
"hasNotExlusiveResolved",
cut=[
op.OR(
op.OR(
op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0),
op.AND(
op.rng_len(bjetsBoosted) >= 1,
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1))
])
hasExlusiveBoostedJets = noSel.refine(
"hasExlusiveBoostedJets",
cut=[
op.rng_len(bjetsBoosted) >= 1,
op.OR(op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)
])
hasNotExlusiveBoostedJets = noSel.refine(
"hasNotExlusiveBoostedJets",
cut=[
op.OR(
op.rng_len(bjetsBoosted) == 0,
op.AND(
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1))
])
# Counting events from different selections for debugging #
# Passing Boosted selection #
PassedBoosted = Plot.make1D("PassedBoosted",
op.c_int(1),
hasBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Boosted',
xTitle='Passed Boosted')
FailedBoosted = Plot.make1D("FailedBoosted",
op.c_int(0),
hasNotBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Boosted',
xTitle='Failed Boosted')
plots.append(
SummedPlot("BoostedCase", [FailedBoosted, PassedBoosted],
xTitle="Boosted selection"))
# Passing Resolved selection #
PassedResolved = Plot.make1D("PassedResolved",
op.c_int(1),
hasResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Resolved',
xTitle='Passed Resolved')
FailedResolved = Plot.make1D("FailedResolved",
op.c_int(0),
hasNotResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Resolved',
xTitle='Failed Resolved')
plots.append(
SummedPlot("ResolvedCase", [FailedResolved, PassedResolved],
xTitle="Resolved selection"))
# Passing Exclusive Resolved (Resolved AND NOT Boosted) #
PassedExclusiveResolved = Plot.make1D(
"PassedExclusiveResolved",
op.c_int(1),
hasExlusiveResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Exclusive Resolved',
xTitle='Passed Exclusive Resolved')
FailedExclusiveResolved = Plot.make1D(
"FailedExclusiveResolved",
op.c_int(0),
hasNotExlusiveResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Exclusive Resolved',
xTitle='Failed Exclusive Resolved')
plots.append(
SummedPlot("ExclusiveResolvedCase",
[FailedExclusiveResolved, PassedExclusiveResolved],
xTitle="Exclusive Resolved selection"))
# Passing Exclusive Boosted (Boosted AND NOT Resolved) #
PassedExclusiveBoosted = Plot.make1D("PassedExclusiveBoosted",
op.c_int(1),
hasExlusiveBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Exclusive Boosted',
xTitle='Passed Exclusive Boosted')
FailedExclusiveBoosted = Plot.make1D("FailedExclusiveBoosted",
op.c_int(0),
hasNotExlusiveBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Exclusive Boosted',
xTitle='Failed Exclusive Boosted')
plots.append(
SummedPlot("ExclusiveBoostedCase",
[FailedExclusiveBoosted, PassedExclusiveBoosted],
xTitle="Exclusive Boosted selection"))
# Passing Boosted AND Resolved #
PassedBoth = Plot.make1D("PassedBoth",
op.c_int(1),
hasBoostedAndResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Both Boosted and Resolved',
xTitle='Passed Boosted and Resolved')
FailedBoth = Plot.make1D(
"FailedBoth", # Means failed the (Boosted AND Resolved) = either one or the other
op.c_int(0),
hasNotBoostedAndResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed combination Boosted and Resolved',
xTitle='Failed combination')
plots.append(
SummedPlot("BoostedAndResolvedCase", [FailedBoth, PassedBoth],
xTitle="Boosted and Resolved selection"))
# Count number of boosted and resolved jets #
plots.append(
Plot.make1D("NBoostedJets",
op.rng_len(bjetsBoosted),
hasBoostedJets,
EquidistantBinning(5, 0., 5.),
title='Number of boosted jets in boosted case',
xTitle='N boosted bjets'))
plots.append(
Plot.make1D(
"NResolvedJets",
op.rng_len(bjetsResolved),
hasExlusiveResolvedJets,
EquidistantBinning(5, 0., 5.),
title='Number of resolved jets in exclusive resolved case',
xTitle='N resolved bjets'))
# Plot number of subjets in the boosted fatjets #
lambda_noSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result == -1,
op.AND(fatjet.subJet2._idx.result == -1))
lambda_oneSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result != -1,
op.AND(fatjet.subJet2._idx.result == -1))
lambda_twoSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result != -1,
op.AND(fatjet.subJet2._idx.result != -1))
hasNoSubjet = hasBoostedJets.refine(
"hasNoSubjet", cut=[lambda_noSubjet(bjetsBoosted[0])])
hasOneSubjet = hasBoostedJets.refine(
"hasOneSubjet", cut=[lambda_oneSubjet(bjetsBoosted[0])])
hasTwoSubjet = hasBoostedJets.refine(
"hasTwoSubjet", cut=[lambda_twoSubjet(bjetsBoosted[0])])
plot_hasNoSubjet = Plot.make1D(
"plot_hasNoSubjet", # Fill bin 0
op.c_int(0),
hasNoSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet without subjet')
plot_hasOneSubjet = Plot.make1D(
"plot_hasOneSubjet", # Fill bin 1
op.c_int(1),
hasOneSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet with one subjet')
plot_hasTwoSubjet = Plot.make1D(
"plot_hasTwoSubjet", # Fill bin 2
op.c_int(2),
hasTwoSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet with two subjets')
plots.append(
SummedPlot(
"NumberOfSubjets",
[plot_hasNoSubjet, plot_hasOneSubjet, plot_hasTwoSubjet],
xTitle="Number of subjets in boosted jet"))
# Plot jets quantities without the dilepton selections #
plots += makeFatJetPlots(self,
sel=hasBoostedJets,
fatjet=bjetsBoosted[0],
suffix="BoostedJets",
channel="NoChannel")
plots += makeJetsPlots(self,
sel=hasResolvedJets,
jets=bjetsResolved,
suffix="ResolvedJets",
channel="NoChannel")
#############################################################################
##################### Jets + Dilepton combination ###########################
#############################################################################
# Combine dilepton and Resolved (Exclusive = NOT Boosted) selections #
hasOsElElOutZResolvedJets = hasOsElElOutZ.refine(
"hasOsElElOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsMuMuOutZResolvedJets = hasOsMuMuOutZ.refine(
"hasOsMuMuOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsElMuOutZResolvedJets = hasOsElMuOutZ.refine(
"hasOsElMuOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsMuElOutZResolvedJets = hasOsMuElOutZ.refine(
"hasOsMuElOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
# Combine dilepton and Boosted selections #
hasOsElElOutZBoostedJets = hasOsElElOutZ.refine(
"hasOsElElOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsMuMuOutZBoostedJets = hasOsMuMuOutZ.refine(
"hasOsMuMuOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsElMuOutZBoostedJets = hasOsElMuOutZ.refine(
"hasOsElMuOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsMuElOutZBoostedJets = hasOsMuElOutZ.refine(
"hasOsMuElOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
# Plot dilepton with OS, Z peak and Resolved jets selections #
plots += makeDileptonPlots(self,
sel=hasOsElElOutZResolvedJets,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZResolvedJets,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZResolvedJets,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZResolvedJets,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='MuEl')
# Plot dilepton with OS dilepton, Z peak and Boosted jets selections #
plots += makeDileptonPlots(self,
sel=hasOsElElOutZBoostedJets,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZBoostedJets,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZBoostedJets,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZBoostedJets,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='MuEl')
# Plotting the fatjet for OS dilepton, Z peak and Boosted jets selections #
plots += makeFatJetPlots(self,
sel=hasOsElElOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="ElEl")
plots += makeFatJetPlots(self,
sel=hasOsMuMuOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="MuMu")
plots += makeFatJetPlots(self,
sel=hasOsElMuOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="ElMu")
plots += makeFatJetPlots(self,
sel=hasOsMuElOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="MuEl")
# Plotting the jets for OS dilepton, Z peak and Resolved jets selections #
plots += makeJetsPlots(self,
sel=hasOsElElOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="ElEl")
plots += makeJetsPlots(self,
sel=hasOsMuMuOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="MuMu")
plots += makeJetsPlots(self,
sel=hasOsElMuOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="ElMu")
plots += makeJetsPlots(self,
sel=hasOsMuElOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="MuEl")
## helper selection (OR) to make sure jet calculations are only done once
#hasOSLL = noSel.refine("hasOSLL", cut=op.OR(*( hasOSLL_cmbRng(rng) for rng in osLLRng.values())))
#forceDefine(t._Jet.calcProd, hasOSLL)
#for varNm in t._Jet.available:
# forceDefine(t._Jet[varNm], hasOSLL)
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
if self.args.examples == "all":
examples = list(i+1 for i in range(self.nExamples)) # 1-4 are fine, so is 7
else:
examples = list(set(int(tk) for tk in self.args.examples.split(",")))
logger.info("Running the following examples: {0}".format(",".join(str(i) for i in examples)))
plots = []
if 1 in examples:
## Example 1: Plot the missing ET of all events.
plots.append(Plot.make1D("Ex1_MET",
tree.MET.pt, noSel,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 2 in examples:
## Example 2: Plot pT of all jets in all events.
plots.append(Plot.make1D("Ex2_jetPt",
op.map(tree.Jet, lambda j : j.pt), noSel,
EqBin(100, 15., 60.), title="Jet p_{T} (GeV/c)"))
if 3 in examples:
## Example 3: Plot pT of jets with |η| < 1.
centralJets1 = op.select(tree.Jet, lambda j : op.abs(j.eta) < 1.)
plots.append(Plot.make1D("Ex3_central1_jetPt",
op.map(centralJets1, lambda j : j.pt), noSel,
EqBin(100, 15., 60.), title="Jet p_{T} (GeV/c)"))
if 4 in examples:
## Example 4: Plot the missing ET of events that have at least two jets with pT > 40 GeV.
jets40 = op.select(tree.Jet, lambda j : j.pt > 40)
hasTwoJets40 = noSel.refine("twoJets40", cut=(op.rng_len(jets40) >= 2))
plots.append(Plot.make1D("Ex4_twoJets40_MET",
tree.MET.pt, hasTwoJets40,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 5 in examples:
## Example 5: Plot the missing ET of events that have an opposite-sign muon pair with an invariant mass between 60 and 120 GeV.
dimu_Z = op.combine(tree.Muon, N=2, pred=(lambda mu1, mu2 : op.AND(
mu1.charge != mu2.charge,
op.in_range(60., op.invariant_mass(mu1.p4, mu2.p4), 120.)
)))
hasDiMuZ = noSel.refine("hasDiMuZ", cut=(op.rng_len(dimu_Z) > 0))
plots.append(Plot.make1D("Ex5_dimuZ_MET",
tree.MET.pt, hasDiMuZ,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 6 in examples:
## Example 6: Plot pT of the trijet system with the mass closest to 172.5 GeV in each event and plot the maximum b-tagging discriminant value among the jets in the triplet.
trijets = op.combine(tree.Jet, N=3)
hadTop = op.rng_min_element_by(trijets,
fun=lambda comb: op.abs((comb[0].p4+comb[1].p4+comb[2].p4).M()-172.5))
hadTop_p4 = (hadTop[0].p4 + hadTop[1].p4 + hadTop[2].p4)
hasTriJet = noSel.refine("hasTriJet", cut=(op.rng_len(trijets) > 0))
plots.append(Plot.make1D("Ex6_trijet_topPt",
hadTop_p4.Pt(), hasTriJet,
EqBin(100, 15., 40.), title="Trijet p_{T} (GeV/c)"))
plots.append(Plot.make1D("Ex6_trijet_maxbtag",
op.max(op.max(hadTop[0].btag, hadTop[1].btag), hadTop[2].btag), hasTriJet,
EqBin(100, 0., 1.), title="Trijet maximum b-tag"))
if verbose:
plots.append(Plot.make1D("Ex6_njets",
op.rng_len(tree.Jet), noSel,
EqBin(20, 0., 20.), title="Number of jets"))
plots.append(Plot.make1D("Ex6_ntrijets",
op.rng_len(trijets), noSel,
EqBin(100, 0., 1000.), title="Number of 3-jet combinations"))
plots.append(Plot.make1D("Ex6_trijet_mass",
hadTop_p4.M(), hasTriJet,
EqBin(100, 0., 250.), title="Trijet mass (GeV/c^{2})"))
if 7 in examples:
## Example 7: Plot the sum of pT of jets with pT > 30 GeV that are not within 0.4 in ΔR of any lepton with pT > 10 GeV.
el10 = op.select(tree.Electron, lambda el : el.pt > 10.)
mu10 = op.select(tree.Muon , lambda mu : mu.pt > 10.)
cleanedJets30 = op.select(tree.Jet, lambda j : op.AND(
j.pt > 30.,
op.NOT(op.rng_any(el10, lambda el : op.deltaR(j.p4, el.p4) < 0.4 )),
op.NOT(op.rng_any(mu10, lambda mu : op.deltaR(j.p4, mu.p4) < 0.4 ))
))
plots.append(Plot.make1D("Ex7_sumCleanedJetPt",
op.rng_sum(cleanedJets30, lambda j : j.pt), noSel,
EqBin(100, 15., 200.), title="Sum p_{T} (GeV/c)"))
if 8 in examples:
## Example 8: For events with at least three leptons and a same-flavor opposite-sign lepton pair, find the same-flavor opposite-sign lepton pair with the mass closest to 91.2 GeV and plot the transverse mass of the missing energy and the leading other lepton.
# The plot is made for each of the different flavour categories (l+/- l-/+ l') and then summed,
# because concatenation of containers is not (yet) supported.
lepColl = { "El" : tree.Electron, "Mu" : tree.Muon }
mt3lPlots = []
for dlNm,dlCol in lepColl.items():
dilep = op.combine(dlCol, N=2, pred=(lambda l1,l2 : op.AND(l1.charge != l2.charge)))
hasDiLep = noSel.refine("hasDilep{0}{0}".format(dlNm), cut=(op.rng_len(dilep) > 0))
dilepZ = op.rng_min_element_by(dilep, fun=lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-91.2))
for tlNm,tlCol in lepColl.items():
if tlCol == dlCol:
hasTriLep = hasDiLep.refine("hasTrilep{0}{0}{1}".format(dlNm,tlNm),
cut=(op.rng_len(tlCol) > 2))
residLep = op.select(tlCol, lambda l : op.AND(l.idx != dilepZ[0].idx, l.idx != dilepZ[1].idx))
l3 = op.rng_max_element_by(residLep, lambda l : l.pt)
else:
hasTriLep = hasDiLep.refine("hasTriLep{0}{0}{1}".format(dlNm,tlNm),
cut=(op.rng_len(tlCol) > 0))
l3 = op.rng_max_element_by(tlCol, lambda l : l.pt)
mtPlot = Plot.make1D("Ex8_3lMT_{0}{0}{1}".format(dlNm,tlNm),
op.sqrt(2*l3.pt*tree.MET.pt*(1-op.cos(l3.phi-tree.MET.phi))), hasTriLep,
EqBin(100, 15., 250.), title="M_{T} (GeV/c^2)")
mt3lPlots.append(mtPlot)
plots.append(mtPlot)
plots.append(SummedPlot("Ex8_3lMT", mt3lPlots))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
plots = []
#definitions
electrons = op.select(t.elec, lambda el : op.AND(
el.pt > 20., op.abs(el.eta) < 2.5
))
muons = op.select(t.muon, lambda mu : op.AND(
mu.pt > 20., op.abs(mu.eta) < 2.5
))
cleanedElectrons = op.select(electrons, lambda el : op.NOT(
op.rng_any(muons, lambda mu : op.deltaR(el.p4, mu.p4) < 0.3 )
))
# we are taking the second isopass to be on which is equal to the medium working point
isolatedElectrons = op.select(cleanedElectrons, lambda el : el.isopass & (1<<2) )
identifiedElectrons = op.select(isolatedElectrons, lambda el : el.idpass & (1<<2) )
cleanedMuons = op.select(muons, lambda mu : op.NOT(
op.rng_any(electrons, lambda el : op.deltaR(mu.p4, el.p4) < 0.3 )
))
isolatedMuons = op.select(cleanedMuons, lambda mu : mu.isopass & (1<<2) )
identifiedMuons = op.select(isolatedMuons, lambda mu : mu.idpass & (1<<2) )
InvMassMuMU = op.invariant_mass(identifiedMuons[0].p4, identifiedMuons[1].p4 )
cleanedJets = op.select(t.jetpuppi, lambda j : op.AND(
op.NOT(op.rng_any(identifiedElectrons, lambda el : op.deltaR(el.p4, j.p4) < 0.3) ),
op.NOT(op.rng_any(identifiedMuons, lambda mu : op.deltaR(mu.p4, j.p4) < 0.3) )
))
cleanedGoodJets = op.select(cleanedJets, lambda j : op.AND(
j.pt > 30, op.abs(j.eta) < 2.5
))
btaggedJets = op.select(cleanedGoodJets, lambda j : j.btag & (1<<2))
met = op.select(t.metpuppi)
#selections
#selection1 : Oppositely charged MuMu selection
sel1 = noSel.refine("nmumu", cut = [op.AND(
(op.rng_len(identifiedMuons) > 1), (op.product(identifiedMuons[0].charge, identifiedMuons[1].charge) < 0 ))])
#selection2 : Invariant mass selection
sel2 = sel1.refine("InvM", cut = [op.NOT(op.in_range(76, InvMassMuMU, 106))])
#selection3 : two jets selection
sel3 = sel2.refine("njet", cut = [op.rng_len(cleanedGoodJets) > 1])
#selection4 : at least 1 among two leading jets is b-tagged
sel4 = sel3.refine("btag", cut = [op.OR(
cleanedGoodJets[0].btag & (1<<2), cleanedGoodJets[1].btag & (1<<2))])
#selection5 : MET > 40 GeV
sel5 = sel4.refine("MET", cut = [met[0].pt > 40])
#plots
#noSel
plots.append(Plot.make1D("nJetsNoSel", op.rng_len(cleanedGoodJets), noSel, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsNoSel", op.rng_len(btaggedJets), noSel, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuNoSel", op.rng_len(identifiedMuons), noSel, EqB(15, 0., 15.), title="nMuons"))
plots.append(Plot.make1D("METptNoSel", met[0].pt, noSel, EqB(50, 0., 250), title="MET_PT"))
#sel1
plots.append(Plot.make1D("nJetsSel1", op.rng_len(cleanedGoodJets), sel1, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel1", op.rng_len(btaggedJets), sel1, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuSel1", op.rng_len(identifiedMuons), sel1, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("InvMassTwoMuonsSel1", InvMassMuMU, sel1, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("LeadingMuonPTSel1", muons[0].pt, sel1, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel1", muons[1].pt, sel1, EqB(30, 0., 250.), title="SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel1", muons[0].eta, sel1, EqB(30, -3, 3), title=" Leading Muon eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel1", muons[1].eta, sel1, EqB(30, -3, 3), title="SubLeading Muon eta"))
plots.append(Plot.make1D("METptSel1", met[0].pt, sel1, EqB(50, 0., 250), title="MET_PT"))
#sel2
plots.append(Plot.make1D("nJetsSel2", op.rng_len(cleanedGoodJets), sel2, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel2", op.rng_len(btaggedJets), sel2, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuSel2", op.rng_len(identifiedMuons), sel2, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("InvMassTwoMuonsSel2", InvMassMuMU, sel2, EqB(20, 20., 300.), title="m(ll)"))
plots.append(Plot.make1D("LeadingMuonPTSel2", muons[0].pt, sel2, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel2", muons[1].pt, sel2, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel2", muons[0].eta, sel2, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel2", muons[1].eta, sel2, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("METptSel2", met[0].pt, sel2, EqB(50, 0., 250), title="MET_PT"))
#sel3
plots.append(Plot.make1D("nJetsSel3", op.rng_len(cleanedGoodJets), sel3, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel3", op.rng_len(btaggedJets), sel3, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel3", cleanedGoodJets[0].pt, sel3, EqB(50, 0., 350.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel3", cleanedGoodJets[1].pt, sel3, EqB(50, 0., 350.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel3", cleanedGoodJets[0].eta, sel3, EqB(30, -3, 3), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel3", cleanedGoodJets[1].eta, sel3, EqB(30, -3, 3), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel3", op.rng_len(identifiedMuons), sel3, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel3", muons[0].pt, sel3, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel3", muons[1].pt, sel3, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel3", muons[0].eta, sel3, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel3", muons[1].eta, sel3, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel3", InvMassMuMU, sel3, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel3", met[0].pt, sel3, EqB(50, 0., 250), title="MET_PT"))
#sel4
plots.append(Plot.make1D("nJetsSel4", op.rng_len(cleanedGoodJets), sel4, EqB(10, 0, 10), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel4", op.rng_len(btaggedJets), sel4, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel4", cleanedGoodJets[0].pt, sel4, EqB(50, 0., 250.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel4", cleanedGoodJets[1].pt, sel4, EqB(50, 0., 250.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel4", cleanedGoodJets[0].eta, sel4, EqB(30, -3, 3.), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel4", cleanedGoodJets[1].eta, sel4, EqB(30, -3, 3.), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel4", op.rng_len(identifiedMuons), sel4, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel4", muons[0].pt, sel4, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel4", muons[1].pt, sel4, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel4", muons[0].eta, sel4, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel4", muons[1].eta, sel4, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel4", InvMassMuMU, sel4, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel4", met[0].pt, sel4, EqB(50, 0., 250), title="MET_PT"))
#sel5
plots.append(Plot.make1D("nJetsSel5", op.rng_len(cleanedGoodJets), sel5, EqB(10, 0, 10), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel5", op.rng_len(btaggedJets), sel5, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel5", cleanedGoodJets[0].pt, sel5, EqB(50, 0., 250.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel5", cleanedGoodJets[1].pt, sel5, EqB(50, 0., 250.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel5", cleanedGoodJets[0].eta, sel5, EqB(30, -3, 3.), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel5", cleanedGoodJets[1].eta, sel5, EqB(30, -3, 3.), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel5", op.rng_len(identifiedMuons), sel5, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel5", muons[0].pt, sel5, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel5", muons[1].pt, sel5, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel5", muons[0].eta, sel5, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel5", muons[1].eta, sel5, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel5", InvMassMuMU, sel5, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel5", met[0].pt, sel5, EqB(50, 0., 250), title="MET_PT > 40"))
# Efficiency Report on terminal and the .tex output
cfr = CutFlowReport("yields")
cfr.add(noSel, "Sel0: No selection")
cfr.add(sel1, "Sel1: nMuMu >= 2")
cfr.add(sel2, "Sel2: InvM")
cfr.add(sel3, "Sel3: nJet >= 2")
cfr.add(sel4, "Sel4: btag")
cfr.add(sel5, "Sel5: MET")
plots.append(cfr)
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.analysisutils import forceDefine
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
era = sampleCfg.get("era") if sampleCfg else None
noSel = noSel.refine("passMETFlags", cut=METFilter(t.Flag, era) )
puWeightsFile = None
if era == "2016":
sfTag="94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2016.json")
elif era == "2017":
sfTag="94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2017.json")
elif era == "2018":
sfTag="102X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2018.json")
if self.isMC(sample) and puWeightsFile is not None:
from bamboo.analysisutils import makePileupWeight
noSel = noSel.refine("puWeight", weight=makePileupWeight(puWeightsFile, t.Pileup_nTrueInt, systName="pileup"))
isMC = self.isMC(sample)
plots = []
forceDefine(t._Muon.calcProd, noSel)
# Wp // 2016- 2017 -2018 : Muon_mediumId // https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2#Muon_Isolation
#To suppress nonprompt lep-tons, the impact parameter in three dimensions of the lepton track, with respect to the primaryvertex, is required to be less than 4 times its uncertainty (|SIP3D|<4)
sorted_muons = op.sort(t.Muon, lambda mu : -mu.pt)
muons = op.select(sorted_muons, lambda mu : op.AND(mu.pt > 10., op.abs(mu.eta) < 2.4, mu.mediumId, mu.pfRelIso04_all<0.15, op.abs(mu.sip3d < 4.)))
# i pass 2016 seprate from 2017 &2018 because SFs need to be combined for BCDEF and GH eras !
if era=="2016":
doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), combine="weight", systName="muid")
muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), combine="weight", systName="muiso")
else:
doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), systName="muid")
muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), systName="muiso")
#Wp // 2016: Electron_cutBased_Sum16==3 -> medium // 2017 -2018 : Electron_cutBased ==3 --> medium ( Fall17_V2)
# asking for electrons to be in the Barrel region with dz<1mm & dxy< 0.5mm // Endcap region dz<2mm & dxy< 0.5mm
# cut-based ID Fall17 V2 the recomended one from POG for the FullRunII
sorted_electrons = op.sort(t.Electron, lambda ele : -ele.pt)
electrons = op.select(sorted_electrons, lambda ele : op.AND(ele.pt > 15., op.abs(ele.eta) < 2.5 , ele.cutBased>=3, op.abs(ele.sip3d)< 4., op.OR(op.AND(op.abs(ele.dxy) < 0.05, op.abs(ele.dz) < 0.1), op.AND(op.abs(ele.dxy) < 0.05, op.abs(ele.dz) < 0.2) )))
elMediumIDSF = get_scalefactor("lepton", ("electron_{0}_{1}".format(era,sfTag), "id_medium"), systName="elid")
doubleEleTrigSF = get_scalefactor("dilepton", ("doubleEleLeg_HHMoriond17_2016"), systName="eleltrig")
elemuTrigSF = get_scalefactor("dilepton", ("elemuLeg_HHMoriond17_2016"), systName="elmutrig")
mueleTrigSF = get_scalefactor("dilepton", ("mueleLeg_HHMoriond17_2016"), systName="mueltrig")
MET = t.MET if era != "2017" else t.METFixEE2017
corrMET=METcorrection(MET,t.PV,sample,era,self.isMC(sample))
####### select jets
##################################
#// 2016 - 2017 - 2018 ( j.jetId &2) -> tight jet ID
# For 2017 data, there is the option of "Tight" or "TightLepVeto", depending on how much you want to veto jets that overlap with/are faked by leptons
sorted_AK4jets=op.sort(t.Jet, lambda j : -j.pt)
AK4jetsSel = op.select(sorted_AK4jets, lambda j : op.AND(j.pt > 20., op.abs(j.eta)< 2.4, (j.jetId &2)))# j.jetId == 6))# oldcut: (j.jetId &2)))
# exclude from the jetsSel any jet that happens to include within its reconstruction cone a muon or an electron.
AK4jets= op.select(AK4jetsSel, lambda j : op.AND(op.NOT(op.rng_any(electrons, lambda ele : op.deltaR(j.p4, ele.p4) < 0.3 )), op.NOT(op.rng_any(muons, lambda mu : op.deltaR(j.p4, mu.p4) < 0.3 ))))
# order jets by *decreasing* deepFlavour
cleaned_AK4JetsByDeepFlav = op.sort(AK4jets, lambda j: -j.btagDeepFlavB)
cleaned_AK4JetsByDeepB = op.sort(AK4jets, lambda j: -j.btagDeepB)
# Boosted Region
sorted_AK8jets=op.sort(t.FatJet, lambda j : -j.pt)
AK8jetsSel = op.select(sorted_AK8jets, lambda j : op.AND(j.pt > 200., op.abs(j.eta)< 2.4, (j.jetId &2), j.subJet1._idx.result != -1, j.subJet2._idx.result != -1))
AK8jets= op.select(AK8jetsSel, lambda j : op.AND(op.NOT(op.rng_any(electrons, lambda ele : op.deltaR(j.p4, ele.p4) < 0.3 )), op.NOT(op.rng_any(muons, lambda mu : op.deltaR(j.p4, mu.p4) < 0.3 ))))
cleaned_AK8JetsByDeepB = op.sort(AK8jets, lambda j: -j.btagDeepB)
# Now, let's ask for the jets to be a b-jets
# DeepCSV or deepJet Medium b-tag working point
btagging = {
"DeepCSV":{ # era: (loose, medium, tight)
"2016":(0.2217, 0.6321, 0.8953),
"2017":(0.1522, 0.4941, 0.8001),
"2018":(0.1241, 0.4184, 0.7527)
},
"DeepFlavour":{
"2016":(0.0614, 0.3093, 0.7221),
"2017":(0.0521, 0.3033, 0.7489),
"2018":(0.0494, 0.2770, 0.7264)
}
}
# bjets ={ "DeepFlavour": {"L": jets pass loose , "M": jets pass medium , "T":jets pass tight }
# "DeepCSV": {"L": --- , "M": --- , "T": ---- }
# }
# FIXME
bjets_boosted = {}
bjets_resolved = {}
#WorkingPoints = ["L", "M", "T"]
WorkingPoints = ["M"]
for tagger in btagging.keys():
bJets_AK4_deepflavour ={}
bJets_AK4_deepcsv ={}
bJets_AK8_deepcsv ={}
# FIXME idx is not propagated properly when i pass only one or two wp !!
for wp in sorted(WorkingPoints):
suffix = ("loose" if wp=='L' else ("medium" if wp=='M' else "tight"))
idx = ( 0 if wp=="L" else ( 1 if wp=="M" else 2))
if tagger=="DeepFlavour":
print ("Btagging: Era= {0}, Tagger={1}, Pass_{2}_working_point={3}".format(era, tagger, suffix, btagging[tagger][era][idx] ))
print ("***********************************************", idx, wp)
print ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepJet', suffix))
bJets_AK4_deepflavour[wp] = op.select(cleaned_AK4JetsByDeepFlav, lambda j : j.btagDeepFlavB >= btagging[tagger][era][idx] )
Jet_DeepFlavourBDisc = { "BTagDiscri": lambda j : j.btagDeepFlavB }
deepBFlavScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepJet', suffix)),
additionalVariables=Jet_DeepFlavourBDisc,
getFlavour=(lambda j : j.hadronFlavour),
systName="btagging{0}".format(era))
bjets_resolved[tagger]=bJets_AK4_deepflavour
else:
print ("Btagging: Era= {0}, Tagger={1}, Pass_{2}_working_point={3}".format(era, tagger, suffix, btagging[tagger][era][idx] ))
print ("***********************************************", idx, wp)
print ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepCSV', suffix))
bJets_AK4_deepcsv[wp] = op.select(cleaned_AK4JetsByDeepB, lambda j : j.btagDeepB >= btagging[tagger][era][idx] )
bJets_AK8_deepcsv[wp] = op.select(cleaned_AK8JetsByDeepB, lambda j : op.AND(j.subJet1.btagDeepB >= btagging[tagger][era][idx] , j.subJet2.btagDeepB >= btagging[tagger][era][idx]))
Jet_DeepCSVBDis = { "BTagDiscri": lambda j : j.btagDeepB }
subJet_DeepCSVBDis = { "BTagDiscri": lambda j : op.AND(j.subJet1.btagDeepB, j.subJet2.btagDeepB) }
# FIXME for boosted and resolved i will use # tagger need to pass jsons files to scale factors above !
deepB_AK4ScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepCSV', suffix)),
additionalVariables=Jet_DeepCSVBDis,
getFlavour=(lambda j : j.hadronFlavour),
systName="btagging{0}".format(era))
# FIXME
#deepB_AK8ScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "subjet_{0}_{1}".format('DeepCSV', suffix)),
#additionalVariables=Jet_DeepCSVBDis,
#getFlavour=(lambda j : j.subJet1.hadronFlavour),
#systName="btagging{0}".format(era))
bjets_resolved[tagger]=bJets_AK4_deepcsv
bjets_boosted[tagger]=bJets_AK8_deepcsv
bestDeepFlavourPair={}
bestDeepCSVPair={}
bestJetPairs= {}
bjets = {}
# For the Resolved only
class GetBestJetPair(object):
JetsPair={}
def __init__(self, JetsPair, tagger, wp):
def ReturnHighestDiscriminatorJet(tagger, wp):
if tagger=="DeepCSV":
return op.sort(safeget(bjets_resolved, tagger, wp), lambda j: - j.btagDeepB)
elif tagger=="DeepFlavour":
return op.sort(safeget(bjets_resolved, tagger, wp), lambda j: - j.btagDeepFlavB)
else:
raise RuntimeError("Something went wrong in returning {0} discriminator !".format(tagger))
firstBest=ReturnHighestDiscriminatorJet(tagger, wp)[0]
JetsPair[0]=firstBest
secondBest=ReturnHighestDiscriminatorJet(tagger, wp)[1]
JetsPair[1]=secondBest
# bestJetPairs= { "DeepFlavour": bestDeepFlavourPair,
# "DeepCSV": bestDeepCSVPair
# }
####### Zmass reconstruction : Opposite Sign , Same Flavour leptons
########################################################
# supress quaronika resonances and jets misidentified as leptons
LowMass_cut = lambda dilep: op.invariant_mass(dilep[0].p4, dilep[1].p4)>12.
## Dilepton selection: opposite sign leptons in range 70.<mll<120. GeV
osdilep_Z = lambda l1,l2 : op.AND(l1.charge != l2.charge, op.in_range(70., op.invariant_mass(l1.p4, l2.p4), 120.))
osLLRng = {
"MuMu" : op.combine(muons, N=2, pred= osdilep_Z),
"ElEl" : op.combine(electrons, N=2, pred=osdilep_Z),
#"ElMu" : op.combine((electrons, muons), pred=lambda ele,mu : op.AND(osdilep_Z(ele,mu), ele.pt > mu.pt )),
#"MuEl" : op.combine((muons, electrons), pred=lambda mu,ele : op.AND(osdilep_Z(mu,ele), mu.pt > ele.pt))
}
hasOSLL_cmbRng = lambda cmbRng : op.AND(op.rng_len(cmbRng) > 0, cmbRng[0][0].pt > 25.) # TODO The leading pT for the µµ channel should be above 20 Gev !
## helper selection (OR) to make sure jet calculations are only done once
hasOSLL = noSel.refine("hasOSLL", cut=op.OR(*( hasOSLL_cmbRng(rng) for rng in osLLRng.values())))
forceDefine(t._Jet.calcProd, hasOSLL)
forceDefine(getattr(t, "_{0}".format("MET" if era != "2017" else "METFixEE2017")).calcProd, hasOSLL)
llSFs = {
"MuMu" : (lambda ll : [ muMediumIDSF(ll[0]), muMediumIDSF(ll[1]), muMediumISOSF(ll[0]), muMediumISOSF(ll[1]), doubleMuTrigSF(ll) ]),
"ElMu" : (lambda ll : [ elMediumIDSF(ll[0]), muMediumIDSF(ll[1]), muMediumISOSF(ll[1]), elemuTrigSF(ll) ]),
"MuEl" : (lambda ll : [ muMediumIDSF(ll[0]), muMediumISOSF(ll[0]), elMediumIDSF(ll[1]), mueleTrigSF(ll) ]),
"ElEl" : (lambda ll : [ elMediumIDSF(ll[0]), elMediumIDSF(ll[1]), doubleEleTrigSF(ll) ])
}
categories = dict((channel, (catLLRng[0], hasOSLL.refine("hasOS{0}".format(channel), cut=hasOSLL_cmbRng(catLLRng), weight=(llSFs[channel](catLLRng[0]) if isMC else None)) )) for channel, catLLRng in osLLRng.items())
## btagging efficiencies plots
#plots.extend(MakeBtagEfficienciesPlots(self, jets, bjets, categories))
for channel, (dilepton, catSel) in categories.items():
#---- Zmass (2Lepton OS && SF ) --------
#plots.extend(MakeControlPlotsForZpic(self, catSel, dilepton, channel))
#---- add Jets selection
TwoLeptonsTwoJets_Resolved = catSel.refine("TwoJet_{0}Sel_resolved".format(channel), cut=[ op.rng_len(AK4jets) > 1 ])
TwoLeptonsTwoJets_Boosted = catSel.refine("OneJet_{0}Sel_boosted".format(channel), cut=[ op.rng_len(AK8jets) > 0 ])
#plots.extend(makeJetPlots(self, TwoLeptonsTwoJets_Resolved, AK4jets, channel))
#plots.extend(makeBoostedJetPLots(self, TwoLeptonsTwoJets_Boosted, AK8jets, channel))
# ----- plots : mll, mlljj, mjj, nVX, pT, eta : basic selection plots ------
#plots.extend(MakeControlPlotsForBasicSel(self, TwoLeptonsTwoJets_Resolved, AK4jets, dilepton, channel))
#plots.extend(MakeControlPlotsForBasicSel(self, TwoLeptonsTwoJets_boosted, AK8jets, dilepton, channel))
for wp in WorkingPoints:
# Get the best AK4 JETS
GetBestJetPair(bestDeepCSVPair,"DeepCSV", wp)
GetBestJetPair(bestDeepFlavourPair,"DeepFlavour", wp)
bestJetPairs["DeepCSV"]=bestDeepCSVPair
bestJetPairs["DeepFlavour"]=bestDeepFlavourPair
print ("bestJetPairs AK4--->", bestJetPairs, wp)
print ("bestJetPairs_deepcsv AK4--->", bestJetPairs["DeepCSV"][0], bestJetPairs["DeepCSV"][1], wp)
print ("bestJetPairs_deepflavour AK4 --->", bestJetPairs["DeepFlavour"][0],bestJetPairs["DeepFlavour"][1], wp)
# resolved
bJets_resolved_PassdeepflavourWP=safeget(bjets_resolved, "DeepFlavour", wp)
bJets_resolved_PassdeepcsvWP=safeget(bjets_resolved, "DeepCSV", wp)
# boosted
bJets_boosted_PassdeepcsvWP=safeget(bjets_boosted, "DeepCSV", wp)
TwoLeptonsTwoBjets_NoMETCut_Res = {
"DeepFlavour{0}".format(wp) : TwoLeptonsTwoJets_Resolved.refine("TwoLeptonsTwoBjets_NoMETcut_DeepFlavour{0}_{1}_Resolved".format(wp, channel),
cut=[ op.rng_len(bJets_resolved_PassdeepflavourWP) > 1 ],
weight=([ deepBFlavScaleFactor(bJets_resolved_PassdeepflavourWP[0]), deepBFlavScaleFactor(bJets_resolved_PassdeepflavourWP[1]) ]if isMC else None)),
"DeepCSV{0}".format(wp) : TwoLeptonsTwoJets_Resolved.refine("TwoLeptonsTwoBjets_NoMETcut_DeepCSV{0}_{1}_Resolved".format(wp, channel),
cut=[ op.rng_len(bJets_resolved_PassdeepcsvWP) > 1 ],
weight=([ deepB_AK4ScaleFactor(bJets_resolved_PassdeepcsvWP[0]), deepB_AK4ScaleFactor(bJets_resolved_PassdeepcsvWP[1]) ]if isMC else None))
}
TwoLeptonsTwoBjets_NoMETCut_Boo = {
"DeepCSV{0}".format(wp) : TwoLeptonsTwoJets_Boosted.refine("TwoLeptonsTwoBjets_NoMETcut_DeepCSV{0}_{1}_Boosted".format(wp, channel),
cut=[ op.rng_len(bJets_boosted_PassdeepcsvWP) > 1 ]),
# FIXME ! can't pass boosted jets SFs with current version ---> move to v7
#weight=([ deepB_AK8ScaleFactor(bJets_boosted_PassdeepcsvWP[0]), deepB_AK8ScaleFactor(bJets_boosted_PassdeepcsvWP[1]) ]if isMC else None))
}
## needed to optimize the MET cut
# FIXME Rerun again &&& pass signal and bkg
# The MET cut is passed to TwoLeptonsTwoBjets selection for the # tagger and for the # wp
plots.extend(MakeMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Res, corrMET, MET, channel, "resolved"))
plots.extend(MakeMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, corrMET, MET, channel, "boosted"))
plots.extend(MakeExtraMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Res, dilepton, MET, channel, "resolved"))
plots.extend(MakeExtraMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, dilepton, MET, channel, "boosted"))
TwoLeptonsTwoBjets_Res = dict((key, selNoMET.refine("TwoLeptonsTwoBjets_{0}_{1}_Resolved".format(key, channel), cut=[ corrMET.pt < 80. ])) for key, selNoMET in TwoLeptonsTwoBjets_NoMETCut_Res.items())
TwoLeptonsTwoBjets_Boo = dict((key, selNoMET.refine("TwoLeptonsTwoBjets_{0}_{1}_Boosted".format(key, channel), cut=[ corrMET.pt < 80. ])) for key, selNoMET in TwoLeptonsTwoBjets_NoMETCut_Boo.items())
#plots.extend(MakeDiscriminatorPlots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, wp, channel, "resolved"))
#plots.extend(MakeDiscriminatorPlots(self, TwoLeptonsTwoBjets_Boo, bjets_boosted, wp, channel, "boosted"))
#plots.extend(makeResolvedBJetPlots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, dilepton, wp, channel))
#plots.extend(makeBoostedBJetPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, bjets_boosted, dilepton, wp, channel))
# --- to get the Ellipses plots
plots.extend(MakeEllipsesPLots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, dilepton, wp, channel, "resolved"))
plots.extend(MakeEllipsesPLots(self, TwoLeptonsTwoBjets_Boo, bjets_boosted, dilepton, wp, channel, "boosted"))
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
plots = []
muons = op.sort(op.select(tree.Muon, lambda mu : op.AND(
mu.pt > 5,
op.abs(mu.eta) < 2.4,
op.abs(mu.pfRelIso04_all) < 0.40,
op.abs(mu.dxy) < 0.5,
op.abs(mu.dz ) < 1.,
op.sqrt(mu.dxy**2 + mu.dz**2)/op.sqrt(mu.dxyErr**2+mu.dzErr**2) < 4, ## SIP3D
)), lambda mu : -mu.pt)
electrons = op.sort(op.select(tree.Electron, lambda el : op.AND(
el.pt > 7.,
op.abs(el.eta) < 2.5,
op.abs(el.pfRelIso03_all) < 0.40,
op.abs(el.dxy) < 0.5,
op.abs(el.dz ) < 1.,
op.sqrt(el.dxy**2 + el.dz**2)/op.sqrt(el.dxyErr**2+el.dzErr**2) < 4, ## SIP3D
)), lambda el : -el.pt)
plots += self.controlPlots_2l(noSel, muons, electrons)
mZ = 91.1876
def reco_4l(leptons, lName, baseSel):
## select events with four leptons, and find the best Z candidate
## shared between 4el and 4mu
has4l = baseSel.refine(f"has4{lName}", cut=[
op.rng_len(leptons) == 4,
op.rng_sum(leptons, lambda l : l.charge) == 0,
])
allZcand = op.combine(leptons, N=2, pred=lambda l1,l2 : l1.charge != l2.charge)
bestZ = op.rng_min_element_by(allZcand, lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-mZ))
otherLeptons = op.select(leptons, partial(lambda l,oz=None : op.AND(l.idx != oz[0].idx, l.idx != oz[1].idx), oz=bestZ))
return has4l, bestZ, otherLeptons
## Mixed category: take leading two for each (as in the other implementations
def cuts2lMixed(leptons):
return [ op.rng_len(leptons) == 2,
leptons[0].charge != leptons[1].charge,
leptons[0].pt > 20.,
leptons[1].pt > 10.
]
has4lMixed = noSel.refine(f"has4lMixed", cut=cuts2lMixed(muons)+cuts2lMixed(electrons))
mixed_mElEl = op.invariant_mass(electrons[0].p4, electrons[1].p4)
mixed_mMuMu = op.invariant_mass(muons[0].p4, muons[1].p4)
## two categories: elel closest to Z or mumu closest to Z
has2El2Mu = has4lMixed.refine(f"has2El2Mu", cut=(op.abs(mixed_mElEl-mZ) < op.abs(mixed_mMuMu-mZ)))
has2Mu2El = has4lMixed.refine(f"has2Mu2El", cut=(op.abs(mixed_mElEl-mZ) > op.abs(mixed_mMuMu-mZ)))
mH_cats = []
for catNm, (has4l, bestZ, otherZ) in {
"4Mu" : reco_4l(muons , "Mu", noSel),
"4El" : reco_4l(electrons, "El", noSel),
"2El2Mu" : (has2El2Mu, electrons, muons),
"2Mu2El" : (has2Mu2El, muons, electrons)
}.items():
bestZp4 = bestZ[0].p4 + bestZ[1].p4
otherZp4 = otherZ[0].p4 + otherZ[1].p4
hasZZ = has4l.refine(f"{has4l.name}ZZ", cut=[
op.deltaR(bestZ[0].p4 , bestZ[1].p4 ) > 0.02,
op.deltaR(otherZ[0].p4, otherZ[1].p4) > 0.02,
op.in_range(40., bestZp4.M(), 120.),
op.in_range(12., otherZp4.M(), 120.)
])
plots += self.controlPlots_4l(hasZZ, bestZ, otherZ)
m4l = (bestZ[0].p4+bestZ[1].p4+otherZ[0].p4+otherZ[1].p4).M()
hasZZ_m4l70 = hasZZ.refine(f"{hasZZ.name}m4l70", cut=(m4l > 70.))
p_mH = Plot.make1D(f"H_mass_{catNm}", m4l, hasZZ_m4l70, EqBin(36, 70., 180.),
plotopts={"show-overflow": False, "log-y": False, "y-axis-range": [0., 18.]})
mH_cats.append(p_mH)
plots.append(p_mH)
plots.append(SummedPlot("H_mass", mH_cats))
return plots