def Apply_VerTexFit(path, lepton, kstar):
if lepton == 'e':
lep_list = ' ^e+:corrected ^e-:corrected'
elif lepton == 'mu':
lep_list = ' ^mu+:all ^mu-:all'
if kstar == 'kst0':
kstlist = ' [K*0:all -> ^K+:all ^pi-:all] '
charge = '0'
elif kstar == 'kst_ks':
kstlist = ' [K*+:ks -> ^K_S0:my_ks ^pi+:all] '
charge = '+'
elif kstar == 'kst_pi0':
kstlist = ' [K*+:pi0 -> ^K+:all ^pi0:all] '
charge = '+'
# Jpsi = ' J/psi' + ':' +lepton+lepton
Bmeson = 'B'+charge+':'+kstar+lepton+lepton
chain = Bmeson+' -> ' + kstlist + lep_list
print(purple, 'vfit: ', cyan, chain, end)
# perform vertex fit of J/psi candidates
# vertex.KFit(Jpsi, conf_level=0.0, path=path)
vertex.KFit(list_name=Bmeson,fit_type='vertex',decay_string=chain, conf_level=0.00, path=path)
def makeFSPs(input_file, isdata):
path = b2.create_path()
inputMdstList(environmentType='default', filelist=input_file, path=path)
printDataStore(path=path)
# path.add_module('EventInfoPrinter')
variables.addAlias('pi_k', 'pidPairProbabilityExpert(211, 321, ALL)')
variables.addAlias('k_pi', 'pidPairProbabilityExpert(321, 211, ALL)')
# electron
impactcut = 'abs(d0)<0.5 and abs(z0)<2.0 '
# NOTE For BtoXll skim
# event level cuts: R2 and require a minimum number of tracks
fillParticleList(decayString='pi+:eventShapeForSkims', cut='pt > 0.1', path=path)
fillParticleList(decayString='gamma:eventShapeForSkims', cut='E > 0.1', path=path)
buildEventShape(inputListNames=['pi+:eventShapeForSkims', 'gamma:eventShapeForSkims'],
allMoments=False,
foxWolfram=True,
harmonicMoments=False,
cleoCones=False,
thrust=False,
collisionAxis=False,
jets=False,
sphericity=False,
checkForDuplicates=False,
path=path)
fillParticleList(decayString='e+:all',
cut=impactcut + ' and electronID > 0.5', path=path)
# muon
fillParticleList(decayString='mu+:all',
cut=impactcut + ' and muonID > 0.5', path=path)
# pion
fillParticleList(decayString='pi+:all',
cut=impactcut + ' and pi_k>0.1', path=path)
# kaon
fillParticleList(decayString='K+:all',
cut=impactcut + ' and k_pi>0.1', path=path)
# gamma
fillParticleList(decayString='gamma:all', cut='', path=path)
if isdata:
print(yellow, 'Momentum correction for FSPs', end)
trackingMomentum(inputListNames=['mu+:all', 'e+:all', 'K+:all', 'pi+:all'], scale=1.00056, path=path)
# Bremrecovery
correctBremsBelle('e-:corrected', 'e-:all', 'gamma:all', True, 0.05, 0.05,
False, path=path)
# Ks0
stdKshorts( path=path)
cutAndCopyList('K_S0:my_ks', 'K_S0:merged', 'goodBelleKshort==1', writeOut=False, path=path)
matchMCTruth(list_name='K_S0:my_ks', path=path)
# pi0
stdPhotons('all', path=path)
cutAndCopyList(
'gamma:eff30_Jan2020',
'gamma:all',
'[clusterReg==1 and E>0.080] or [clusterReg==2 and E>0.030] or [clusterReg==3 and E>0.060]',
path=path)
reconstructDecay('pi0:all -> gamma:eff30_Jan2020 gamma:eff30_Jan2020',
'0.1215 < M < 0.1415 and E > 0.4 and abs(cosHelicityAngleMomentum)<0.8', path=path)
#vertex.KFit('pi0:all', conf_level=0.0, path=path)
vertex.KFit('pi0:all', conf_level=0.0,fit_type='mass', path=path)
# K*0->K+ pi-
reconstructDecay(decayString='K*0:all -> K+:all pi-:all',
cut='0.6 < M < 1.2', path=path)
matchMCTruth(list_name='K*0:all', path=path)
# K*+ -> K_s pi+
reconstructDecay(decayString='K*+:ks -> K_S0:my_ks pi+:all',
cut='0.6 < M < 1.2', path=path)
matchMCTruth(list_name='K*+:ks', path=path)
# K*+ -> K+ pi0
reconstructDecay(decayString='K*+:pi0 -> K+:all pi0:all',
cut='0.6 < M < 1.2', path=path)
matchMCTruth(list_name='K*+:pi0', path=path)
return path
"passesCut(clusterReg == 2 and clusterE > 0.05)")
variables.addAlias("goodBWDGamma",
"passesCut(clusterReg == 3 and clusterE > 0.1)")
variables.addAlias('goodGamma',
'passesCut(goodFWDGamma or goodBRLGamma or goodBWDGamma)')
ma.fillParticleList('gamma:brems', 'goodGamma', path=main)
ma.correctBrems('e+:corrected', 'e+:uncorrected', 'gamma:brems', path=main)
variables.addAlias('isBremsCorrected', 'extraInfo(bremsCorrected)')
# combine final state particles to form composite particles
ma.reconstructDecay('J/psi:ee -> e+:corrected e-:corrected ?addbrems',
cut='dM < 0.11',
path=main)
# perform vertex fit of J/psi candidates
vertex.KFit('J/psi:ee', conf_level=0.0, path=main)
# combine J/psi and KS candidates to form B0 candidates
ma.reconstructDecay('B0 -> J/psi:ee K_S0:merged',
cut='Mbc > 5.2 and abs(deltaE) < 0.3',
path=main)
# match reconstructed with MC particles
ma.matchMCTruth('B0', path=main)
# build the rest of the event
ma.buildRestOfEvent('B0', fillWithMostLikely=True, path=main)
# call flavor tagging
ft.flavorTagger('B0', path=main)