def mAnalysis(photons, electrons, muons, nu_es, nu_ms, h2M3M4, suffix):
# Leading/Sub-leading Photon Analysis
leptons = electrons + muons
nus = nu_es + nu_ms
leadPhoton = selectLead(photons)
subPhoton = selectSub(photons)
lepton = selectLead(leptons) # Let signal lepton be the one w/ highest PT
nu = selectLead(nus)
if leadPhoton != None :
histogramBuilder.fillPtHistograms([leadPhoton],
'Photon(Leading)_Pt_' + suffix)
histogramBuilder.fillEtaHistograms([leadPhoton],
'Photon(Leading)_Eta_' + suffix)
# Near-Z-Mass checks
if(leadPhoton != None and subPhoton != None and lepton != None):
m2_L = (leadPhoton + lepton).M()
m2_S = (subPhoton + lepton).M()
m3 = (leadPhoton + subPhoton + lepton).M()
histogramBuilder.fillMHistograms(m2_L, 'Z2M(A_Lead+L)_' + suffix)
histogramBuilder.fillMHistograms(m2_S, 'Z2M(A_Sub+L)_' + suffix)
histogramBuilder.fillMHistograms(m3, 'Z3M(A+A+L)_' + suffix)
# Near-W-Mass checks
if(leadPhoton != None and subPhoton != None and lepton != None
and nu != None):
m3 = (leadPhoton + lepton + nu).M() # no sub leading lepton
m4 = (leadPhoton + subPhoton + lepton + nu).M()
histogramBuilder.fillMHistograms(m3, '3M(A+L+Nu)_' + suffix)
histogramBuilder.fillMHistograms(m4, '4M(A+A+L+Nu)_' + suffix)
h2M3M4.Fill(m3, m4)
def selectOnElectronKinematics(electrons):
#if electrons != None:
electrons = filter(lambda electron: electron.Pt() > minElectronPt, electrons)
histogramBuilder.fillPtHistograms(electrons, 'Electron_Pt_PostElectronPtCut')
electrons = filter(lambda electron: abs(electron.Eta()) < maxElectronEta, electrons)
histogramBuilder.fillEtaHistograms(electrons, 'Electron_Eta_PostElectronEtaCut')
return electrons
def MakeHistograms(tree, channel, weight):
histogramBuilder.fillPtHistograms(channel, tree.pt_leadph12, weight)
histogramBuilder.fillPtCategoryHistograms(channel, tree.pt_leadph12, weight)
# Hack to Change Selection to Photon Pt > 40
if(tree.pt_leadph12 > 40):
histogramBuilder.fillCountHistograms(channel, weight)
def MakeHistograms(tree, channel, aqgc_weight_index, aqgc_weight, sm_weight):
histogramBuilder.fillCountHistograms(
channel + "_All_aQGC_Weight_" + str(aqgc_weight_index), aqgc_weight)
histogramBuilder.fillPtHistograms(
channel + "_All_LeadPhoton_aQGC_Weight_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight)
histogramBuilder.fillPtHistograms(
channel + "_All_SublPhoton_aQGC_Weight_" + str(aqgc_weight_index),
tree.pt_sublph12, aqgc_weight)
histogramBuilder.fill2DPtHistograms(
channel + "_All_LeadAndSublPhoton_aQGC_Weight_" +
str(aqgc_weight_index), tree.pt_leadph12, tree.pt_sublph12,
aqgc_weight)
histogramBuilder.fillPtCategoryHistograms(
channel + "All_LeadPhoton_aQGC_Weight_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight)
MakeCovarianceHistograms(tree, channel + "_All", aqgc_weight_index,
aqgc_weight, sm_weight)
# Make Separate Histograms for EBEB, EB EE, and EE EB Cateogries
MakePhotonLocationHistograms(tree, channel, aqgc_weight_index, aqgc_weight,
sm_weight)
# Make Histograms for Different Cuts on the SubLeading Photon Pt
MakeSubPhotonCutHistograms(tree, channel, aqgc_weight_index, aqgc_weight,
sm_weight)
def MakeHistograms(prefix, photons, weight):
leadPhoton = selectLead(photons)
histogramBuilder.fillPtHistograms(prefix, leadPhoton.Pt(), weight)
histogramBuilder.fillPtCategoryHistograms(prefix, leadPhoton.Pt(), weight)
if(leadPhoton.Pt() > 40):
histogramBuilder.fillCountHistograms(prefix, weight)
def MakeCovarianceHistograms(tree, channel, aqgc_weight_index, aqgc_weight,
sm_weight):
histogramBuilder.fillPtCategoryHistograms(
channel + "_aQGC_Covariance_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight * sm_weight)
histogramBuilder.fillPtHistograms(
channel + "_aQGC_Covariance_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight * sm_weight)
"""
def MakeMtHistograms(prefix, leptons, neutrinos):
summed_nu=TLorentzVector()
for nu in neutrinos:
summed_nu += nu
histogramBuilder.fillPtHistograms(prefix+"Summed_Nu", summed_nu.Pt())
for lepton in leptons:
Mt2 = 2*lepton.Et()*summed_nu.Et()*(1-cos(lepton.DeltaPhi(summed_nu)))
Mt = sqrt(Mt2)
histogramBuilder.fillMtHistograms(prefix, Mt)
def MakeParticleTypeHistograms(prefix, particles, particleType):
histogramBuilder.fillMultiplicityHistograms(prefix+"_"+particleType, len(particles))
for particle in particles:
histogramBuilder.fillPtHistograms(prefix+"_"+particleType,particle.Pt())
histogramBuilder.fillEtaHistograms(prefix+"_"+particleType, particle.Eta())
histogramBuilder.fillStatusHistograms(prefix+"_" + particleType, particle.Status())
histogramBuilder.fillPDGIDHistograms(prefix+"_"+particleType, particle.PID())
histogramBuilder.fillPDGIDHistograms(prefix+"_"+particleType+"Mother", particle.MomPID())
def selectOnPhotonKinematics(photons):
#if photons != None:
# Post Cut Histograms
photons = filter(lambda photon: photon.Pt() > minPhotonPt, photons)
histogramBuilder.fillPtHistograms(photons, 'Photon_Pt_PostPhotonPtCut')
photons = filter(lambda photon: abs(photon.Eta()) < maxPhotonEta, photons)
#photons = filter(lambda photon: abs(photon.Eta()) < minPhotonEndCapEta
# or abs(photon.Eta()) > maxPhotonEndCapEta, photons)
histogramBuilder.fillEtaHistograms(photons, 'Photon_Eta_PostPhotonEtaCut')
return photons
def MakePhotonLocationHistograms(tree, channel, aqgc_weight_index, aqgc_weight,
sm_weight):
if (tree.isEB_leadph12 and tree.isEB_sublph12):
histogramBuilder.fillPtHistograms(
channel + "_EBEB_aQGC_Weight_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight)
MakeCovarianceHistograms(tree, channel + "_EBEB", aqgc_weight_index,
aqgc_weight, sm_weight)
if (tree.isEB_leadph12 and tree.isEE_sublph12):
histogramBuilder.fillPtHistograms(
channel + "_EBEE_aQGC_Weight_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight)
MakeCovarianceHistograms(tree, channel + "_EBEE", aqgc_weight_index,
aqgc_weight, sm_weight)
#histogramBuilder.fillPtHistograms(channel+"_EBEEandEEEB_aQGC_Weight_"+str(aqgc_weight_index), tree.pt_leadph12, aqgc_weight)
if (tree.isEE_leadph12 and tree.isEB_sublph12):
histogramBuilder.fillPtHistograms(
channel + "_EEEB_aQGC_Weight_" + str(aqgc_weight_index),
tree.pt_leadph12, aqgc_weight)
MakeCovarianceHistograms(tree, channel + "_EEEB", aqgc_weight_index,
aqgc_weight, sm_weight)
def selectOnMuonParent(muons):
#if muons != None:
muons = filter(lambda muon: abs(muon.MomPID()) == 24, muons)
histogramBuilder.fillPtHistograms(muons, 'Muon_Pt_PostWParentCut')
histogramBuilder.fillEtaHistograms(muons, 'Muon_Eta_PostWParentCut')
return muons
def selectOnElectronParent(electrons):
#if electrons != None:
electrons = filter(lambda electron: abs(electron.MomPID()) == 24, electrons)
histogramBuilder.fillPtHistograms(electrons, 'Electron_Pt_PostWParentCut')
histogramBuilder.fillEtaHistograms(electrons, 'Electron_Eta_PostWParentCut')
return electrons
def selectOnPhotonParent(photons):
#if photons != None:
filter_photons = filter(lambda photon: abs(photon.MomPID()) < 25, photons)
histogramBuilder.fillPtHistograms(filter_photons, 'Photon_Pt_PostParentCut')
histogramBuilder.fillEtaHistograms(filter_photons, 'Photon_Eta_PostParentCut')
return filter_photons
def selectOnMuonKinematics(muons):
muons = filter(lambda muon: muon.Pt() > minMuonPt, muons)
histogramBuilder.fillPtHistograms(muons, 'Muon_Pt_PostMuonPtCut')
muons = filter(lambda muon: abs(muon.Eta()) < maxMuonEta, muons)
histogramBuilder.fillEtaHistograms(muons, 'Muon_Eta_PostMuonEtaCut')
return muons