def _estimate(self, region, channel, setup):
if setup.verbose:
printHeader("MC prediction for %s channel %s" %
(self.name, channel))
if channel == 'all':
return sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
], u_float(0., 0.))
else:
preSelection = setup.preselection('MC', channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s" % (cut, weight)
if not self.sample[channel].has_key('chain'):
loadChain(self.sample[channel])
return setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(self.sample[channel]['chain'],
cutString=cut,
weight=weight,
returnError=True))
def _estimate(self, region, channel, setup):
''' Concrete implementation of abstract method 'estimate' as defined in Systematic
'''
logger.debug("MC prediction for %s channel %s" % (self.name, channel))
if channel == 'all':
# 'all' is the total of all contributions
return sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
])
else:
# Important! We use 'allZ' (mll>20) in case of EMu
zWindow = 'allZ' if channel == 'EMu' else 'offZ'
preSelection = setup.preselection('MC',
zWindow=zWindow,
channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
logger.debug("Using cut %s and weight %s" % (cut, weight))
return setup.lumi[channel] / 1000. * u_float(
**self.sample[channel].getYieldFromDraw(selectionString=cut,
weightString=weight))
def yieldFromCache(self, setup, sample, channel, selectionString, weightString):
s = (sample, channel, selectionString, weightString)
if self.helperCache.contains(s):
return self.helperCache.get(s)
else:
yieldFromDraw = u_float(**setup.sample[sample][channel].getYieldFromDraw(selectionString, weightString))
self.helperCache.add(s, yieldFromDraw, save=True)
return yieldFromDraw
def yieldFromCache(self, setup, sample, channel, selectionString,
weightString):
s = (sample, channel, selectionString, weightString)
if self.helperCache.contains(s):
return self.helperCache.get(s)
else:
yieldFromDraw = u_float(
**setup.sample[sample][channel].getYieldFromDraw(
selectionString, weightString))
self.helperCache.add(s, yieldFromDraw, save=True)
return yieldFromDraw
def _estimate(self, region, channel, setup):
if setup.verbose: printHeader("MC prediction for %s channel %s" %(self.name, channel))
if channel=='all':
return sum( [ self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu'] ], u_float(0., 0.) )
else:
preSelection = setup.preselection('MC', channel=channel)
cut = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s"%(cut, weight)
if not self.sample[channel].has_key('chain'):
loadChain(self.sample[channel])
return setup.lumi[channel]/1000.*u_float(getYieldFromChain(self.sample[channel]['chain'], cutString = cut, weight=weight, returnError = True) )
def _estimate(self, region, channel, setup):
printHeader("DD DY prediction for %s channel %s" %(self.name, channel))
#Sum of all channels for 'all'
if channel=='all':
return sum( [ self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu'] ], u_float(0.,0.) )
#MC based for 'EMu'
elif channel=='EMu':
preSelection = setup.preselection('MC', zWindow="allZ", channel=channel)
cut = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut'] ])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s"%(cut, weight)
return setup.lumi[channel]/1000. * u_float( getYieldFromChain(setup.sample['DY'][channel]['chain'], cutString = cut, weight=weight, returnError = True) )
#Data driven for EE and MuMu
else:
preSelection = setup.preselection('MC', zWindow="offZ", channel=channel)
weight = preSelection['weightStr']
assert abs(1.-setup.lumi[channel]/setup.sample['Data'][channel]['lumi'])<0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s"%(setup.lumi[channel], setup.sample['Data'][channel]['lumi'], channel)
cut_offZ_1b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('MC', channel=channel, zWindow = 'offZ', **setup.defaultParameters(update={'nBTags':(1,-1)}))['cut'] ])
cut_onZ_1b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('MC', channel=channel, zWindow = 'onZ', **setup.defaultParameters(update={'nBTags':(1,-1)}))['cut'] ])
cut_onZ_0b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('MC', channel=channel, zWindow = 'onZ', **setup.defaultParameters(update={'nBTags':(0,0)}))['cut'] ])
cut_data_onZ_0b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('Data', channel=channel, zWindow = 'onZ', **setup.defaultParameters(update={'nBTags':(0,0)}) )['cut'] ])
# R1 = DY-MC (offZ, 1b) / DY-MC (onZ, 1b)
# R2 = DY-MC (onZ, 1b) / DY-MC (onZ, 0b)
# DY-est = R1*R2*(Data(2l, onZ, 0b) - EWK(onZ, 0b)) = DY-MC (offZ, 1b) / DY-MC (onZ, 0b) *( Data(2l, onZ, 0b) - EWK(onZ, 0b))
yield_offZ_1b = setup.lumi[channel]/1000.*u_float( getYieldFromChain(setup.sample['DY'][channel]['chain'], cutString = cut_offZ_1b, weight=weight, returnError = True))
if setup.verbose: print "yield_offZ_1b: %s"%yield_offZ_1b
yield_onZ_0b = setup.lumi[channel]/1000.*u_float( getYieldFromChain(setup.sample['DY'][channel]['chain'], cutString = cut_onZ_0b, weight=weight, returnError = True))
if setup.verbose: print "yield_onZ_0b: %s"%yield_onZ_0b
yield_data = u_float( getYieldFromChain(setup.sample['Data'][channel]['chain'], cutString = cut_data_onZ_0b, weight=weight, returnError = True))
if setup.verbose: print "yield_data: %s (for cut: %s \n with weight: %s)"%(yield_data, cut_data_onZ_0b, weight)
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets' , 'TTZ' , 'other']:
yield_other += setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample[s][channel]['chain'], cutString = cut_onZ_0b, weight=weight, returnError=True))
if setup.verbose: print "yield_other_onZ_0b %s added, now: %s"%(s, yield_other)
normRegYield = yield_data - yield_other
if normRegYield.val<0: print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n"%(yield_data, yield_other)
mcRatio = yield_offZ_1b / yield_onZ_0b
res = mcRatio * normRegYield
print "mcRatio is: ", mcRatio
return res
def _estimate(self, region, channel, setup):
''' Concrete implementation of abstract method 'estimate' as defined in Systematic
'''
logger.debug( "MC prediction for %s channel %s" %(self.name, channel) )
if channel=='all':
# 'all' is the total of all contributions
return sum( [ self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu'] ], u_float(0., 0.) )
else:
# Important! We use 'allZ' (mll>20) in case of EMu
zWindow= 'allZ' if channel=='EMu' else 'offZ'
preSelection = setup.preselection('MC', zWindow=zWindow, channel=channel)
cut = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
logger.debug( "Using cut %s and weight %s"%(cut, weight) )
return setup.lumi[channel]/1000.*u_float(**self.sample[channel].getYieldFromDraw(selectionString = cut, weightString = weight) )
def _estimate(self, region, channel, setup):
printHeader("DD DY prediction for %s channel %s" %
(self.name, channel))
#Sum of all channels for 'all'
if channel == 'all':
return sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
], u_float(0., 0.))
#MC based for 'EMu'
elif channel == 'EMu':
preSelection = setup.preselection('MC',
zWindow="allZ",
channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s" % (cut, weight)
return setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['DY'][channel]['chain'],
cutString=cut,
weight=weight,
returnError=True))
#Data driven for EE and MuMu
else:
preSelection = setup.preselection('MC',
zWindow="offZ",
channel=channel)
weight = preSelection['weightStr']
assert abs(
1. -
setup.lumi[channel] / setup.sample['Data'][channel]['lumi']
) < 0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s" % (
setup.lumi[channel], setup.sample['Data'][channel]['lumi'],
channel)
cut_offZ_1b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='offZ',
**setup.defaultParameters(update={'nBTags': (1,
-1)}))['cut']
])
cut_onZ_1b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (1,
-1)}))['cut']
])
cut_onZ_0b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (0,
0)}))['cut']
])
cut_data_onZ_0b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'Data',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (0,
0)}))['cut']
])
# R1 = DY-MC (offZ, 1b) / DY-MC (onZ, 1b)
# R2 = DY-MC (onZ, 1b) / DY-MC (onZ, 0b)
# DY-est = R1*R2*(Data(2l, onZ, 0b) - EWK(onZ, 0b)) = DY-MC (offZ, 1b) / DY-MC (onZ, 0b) *( Data(2l, onZ, 0b) - EWK(onZ, 0b))
yield_offZ_1b = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['DY'][channel]['chain'],
cutString=cut_offZ_1b,
weight=weight,
returnError=True))
if setup.verbose: print "yield_offZ_1b: %s" % yield_offZ_1b
yield_onZ_0b = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['DY'][channel]['chain'],
cutString=cut_onZ_0b,
weight=weight,
returnError=True))
if setup.verbose: print "yield_onZ_0b: %s" % yield_onZ_0b
yield_data = u_float(
getYieldFromChain(setup.sample['Data'][channel]['chain'],
cutString=cut_data_onZ_0b,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data: %s (for cut: %s \n with weight: %s)" % (
yield_data, cut_data_onZ_0b, weight)
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets', 'TTZ', 'other']:
yield_other += setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample[s][channel]['chain'],
cutString=cut_onZ_0b,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_other_onZ_0b %s added, now: %s" % (
s, yield_other)
normRegYield = yield_data - yield_other
if normRegYield.val < 0:
print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n" % (
yield_data, yield_other)
mcRatio = yield_offZ_1b / yield_onZ_0b
res = mcRatio * normRegYield
print "mcRatio is: ", mcRatio
return res
def _estimate(self, region, channel, setup):
# Sum of all channels for 'all'
if channel == "all":
estimate = sum([self.cachedEstimate(region, c, setup) for c in ["MuMu", "EE", "EMu"]])
# MC based for 'EMu'
elif channel == "EMu":
weight = setup.weightString()
preSelection = setup.preselection("MC", zWindow="allZ", channel=channel)
cut = "&&".join([region.cutString(setup.sys["selectionModifier"]), preSelection["cut"]])
estimate = (
setup.lumi[channel]
/ 1000.0
* u_float(**setup.sample["DY"][channel].getYieldFromDraw(selectionString=cut, weightString=weight))
)
# Data driven for EE and MuMu (calculate for data luminosity)
else:
weight = setup.weightString()
cut_offZ_1b = "&&".join(
[
region.cutString(setup.sys["selectionModifier"]),
setup.selection(
"MC", channel=channel, zWindow="offZ", **setup.defaultParameters(update={"nBTags": (1, -1)})
)["cut"],
]
)
cut_onZ_0b = "&&".join(
[
region.cutString(setup.sys["selectionModifier"]),
setup.selection(
"MC", channel=channel, zWindow="onZ", **setup.defaultParameters(update={"nBTags": (0, 0)})
)["cut"],
]
)
cut_data_onZ_0b = "&&".join(
[
region.cutString(),
setup.selection(
"Data", channel=channel, zWindow="onZ", **setup.defaultParameters(update={"nBTags": (0, 0)})
)["cut"],
]
)
# Calculate ratio (offZ,1b)/(onZ,0b)
yield_offZ_1b = (
u_float(
**setup.sample["DY"][channel].getYieldFromDraw(selectionString=cut_offZ_1b, weightString=weight)
)
* setup.dataLumi[channel]
/ 1000
)
yield_onZ_0b = (
u_float(**setup.sample["DY"][channel].getYieldFromDraw(selectionString=cut_onZ_0b, weightString=weight))
* setup.dataLumi[channel]
/ 1000
)
R = yield_offZ_1b / yield_onZ_0b if yield_onZ_0b > 0 else 0
# Calculate data-other onZ for 0 b-jets region
yield_data = u_float(
**setup.sample["Data"][channel].getYieldFromDraw(selectionString=cut_data_onZ_0b, weightString="(1)")
)
yield_other = (
sum(
u_float(
**setup.sample[s][channel].getYieldFromDraw(selectionString=cut_onZ_0b, weightString=weight)
)
for s in ["TTJets", "TTZ", "other"]
)
* setup.dataLumi[channel]
/ 1000
)
normRegYield = yield_data - yield_other
# Calculate DY estimate in 1 b-jet region (and scale back to MC lumi)
estimate = R * normRegYield * setup.lumi[channel] / setup.dataLumi[channel]
logger.info(
"Calculating data-driven DY estimate in channel "
+ channel
+ " using lumi "
+ str(setup.dataLumi[channel])
+ ":"
)
logger.info("yield DY offZ/1b: " + str(yield_offZ_1b))
logger.info("yield DY onZ/0b: " + str(yield_onZ_0b))
logger.info("R: " + str(R))
logger.info("yield data onZ/0b: " + str(yield_data))
logger.info("yield other onZ/0b: " + str(yield_other))
logger.info("yield (data-other) onZ/0b: " + str(normRegYield))
logger.info("yield expected DY onZ/1b: " + str(normRegYield * R))
if normRegYield < 0 and yield_data > 0:
logger.warn("Negative normalization region yield!")
logger.info(
"Estimate for DY in "
+ channel
+ " channel (lumi="
+ str(setup.lumi[channel])
+ "/pb): "
+ str(estimate)
+ (" (negative estimated being replaced by 0)" if estimate < 0 else "")
)
return estimate if estimate > 0 else u_float(0, 0)
def _estimate(self, region, channel, setup):
printHeader("DD TTZ prediction for '%s' channel %s" %(self.name, channel))
#Sum of all channels for 'all'
if channel=='all':
return sum( [ self.cachedEstimate(region, c, channel, setup) for c in ['MuMu', 'EE', 'EMu'] ] )
else:
#Data driven for EE, EMu and MuMu.
preSelection = setup.preselection('MC', channel=channel)
#check lumi consistency
assert abs(1.-setup.lumi[channel]/setup.sample['Data'][channel]['lumi'])<0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s"%(setup.lumi[channel], setup.sample['Data'][channel]['lumi'], channel)
selection_MC_2l = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
yield_MC_2l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = selection_MC_2l, weight=weight, returnError = True) )
if setup.verbose: print "yield_MC_2l: %s"%yield_MC_2l
muonSelection_loosePt = looseMuIDString(ptCut=10)
electronSelection_loosePt = looseEleIDString(ptCut=10)
#mu_mu_mu
MuMuMuSelection = "nGoodMuons>=2" + '&&' + muonSelection_loosePt + "==3"
if setup.parameters['useTriggers']: MuMuMuSelection += '&&HLT_3mu'
#e_e_e
EEESelection = "nGoodElectrons>=2" + '&&' + electronSelection_loosePt + "==3"
if setup.parameters['useTriggers']: EEESelection += '&&HLT_3e'
#e_e_mu
EEMuSelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==2&&" + muonSelection_loosePt + "==1"
if setup.parameters['useTriggers']: EEMuSelection += '&&HLT_2e1mu'
#mu_mu_e
MuMuESelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==1&&" + muonSelection_loosePt + "==2"
if setup.parameters['useTriggers']: MuMuESelection += '&&HLT_2mu1e'
MC_hadronSelection = setup.selection('MC', hadronicSelection = True,
**setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. })
)['cut']
data_hadronSelection = setup.selection('Data', hadronicSelection = True,
**setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. })
)['cut']
#loose bjet selection added here
if self.nLooseBTags[0]>=0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>='+str(self.nLooseBTags[0])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>='+str(self.nLooseBTags[0])
if self.nLooseBTags[1]>=0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<='+str(self.nLooseBTags[1])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<='+str(self.nLooseBTags[1])
MC_MuMuMu = "&&".join([
MC_hadronSelection,
MuMuMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_EEE = "&&".join([
MC_hadronSelection,
EEESelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_EEMu = "&&".join([
MC_hadronSelection,
EEMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_MuMuE = "&&".join([
MC_hadronSelection,
MuMuESelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_3l = "(("+MC_MuMuMu+")||("+MC_EEE+")||("+MC_EEMu+")||("+MC_MuMuE+"))"
data_MuMuMu = "&&".join([
data_hadronSelection,
MuMuMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
data_EEE = "&&".join([
data_hadronSelection,
EEESelection,
"abs(mlmZ_mass-91.2)<10"
])
data_EEMu = "&&".join([
data_hadronSelection,
EEMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
data_MuMuE = "&&".join([
data_hadronSelection,
MuMuESelection,
"abs(mlmZ_mass-91.2)<10"
])
######yield_MC_3l computed for ALL channels but lumi changes slightly here depending on channel
yield_MC_3l = setup.lumi[channel]/1000.*u_float( getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = MC_3l, weight=weight, returnError = True))
if setup.verbose: print "yield_MC_looseSelection_3l: %s"%yield_MC_3l
yield_data_MuMuMu = u_float( getYieldFromChain(setup.sample['Data']['MuMu']['chain'], cutString = data_MuMuMu, weight=weight, returnError = True))
if setup.verbose: print "yield_data_looseSelection_MuMuMu: %s"%yield_data_MuMuMu
yield_data_EEE = u_float( getYieldFromChain(setup.sample['Data']['EE']['chain'], cutString = data_EEE, weight=weight, returnError = True))
if setup.verbose: print "yield_data_looseSelection_EEE: %s"%yield_data_EEE
yield_data_EMu = u_float( getYieldFromChain(setup.sample['Data']['EMu']['chain'], cutString = "(("+data_MuMuE+')||('+data_EEMu+'))', weight=weight, returnError = True))
if setup.verbose: print "yield_data_looseSelection_EMu: %s"%yield_data_EMu
yield_data_3l = yield_data_MuMuMu+yield_data_EEE+yield_data_EMu
if setup.verbose: print "yield_data_3l: %s"%yield_data_3l
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets' , 'DY', 'other']:
yield_other+= setup.lumi[channel]/1000.* u_float(getYieldFromChain(setup.sample[s][channel]['chain'], cutString = MC_3l, weight=weight, returnError=True))
if setup.verbose: print "yield_looseSelection_other %s added, now: %s"%(s, yield_other)
normRegYield = yield_data_3l - yield_other
if normRegYield.val<0: print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n"%(yield_data_3l, yield_other)
print "normRegYield", normRegYield
print "\n Control Region predictys ", normRegYield, " TTZ events in data; ", yield_MC_3l, " TTZ events in MC. Ratio ---> ", (normRegYield/yield_MC_3l)
print "DD-TTZ ---> ", (normRegYield/yield_MC_3l)*yield_MC_2l
return (normRegYield/yield_MC_3l)*yield_MC_2l
def _estimate(self, region, channel, setup):
printHeader("DD TTZ prediction for '%s' channel %s" %
(self.name, channel))
#Sum of all channels for 'all'
if channel == 'all':
return sum([
self.cachedEstimate(region, c, channel, setup)
for c in ['MuMu', 'EE', 'EMu']
])
else:
#Data driven for EE, EMu and MuMu.
preSelection = setup.preselection('MC', channel=channel)
#check lumi consistency
assert abs(
1. -
setup.lumi[channel] / setup.sample['Data'][channel]['lumi']
) < 0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s" % (
setup.lumi[channel], setup.sample['Data'][channel]['lumi'],
channel)
selection_MC_2l = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
yield_MC_2l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=selection_MC_2l,
weight=weight,
returnError=True))
if setup.verbose: print "yield_MC_2l: %s" % yield_MC_2l
muonSelection_loosePt = looseMuIDString(ptCut=10)
electronSelection_loosePt = looseEleIDString(ptCut=10)
#mu_mu_mu
MuMuMuSelection = "nGoodMuons>=2" + '&&' + muonSelection_loosePt + "==3"
if setup.parameters['useTriggers']: MuMuMuSelection += '&&HLT_3mu'
#e_e_e
EEESelection = "nGoodElectrons>=2" + '&&' + electronSelection_loosePt + "==3"
if setup.parameters['useTriggers']: EEESelection += '&&HLT_3e'
#e_e_mu
EEMuSelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==2&&" + muonSelection_loosePt + "==1"
if setup.parameters['useTriggers']: EEMuSelection += '&&HLT_2e1mu'
#mu_mu_e
MuMuESelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==1&&" + muonSelection_loosePt + "==2"
if setup.parameters['useTriggers']: MuMuESelection += '&&HLT_2mu1e'
MC_hadronSelection = setup.selection(
'MC',
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
data_hadronSelection = setup.selection(
'Data',
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
#loose bjet selection added here
if self.nLooseBTags[0] >= 0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>=' + str(
self.nLooseBTags[0])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>=' + str(
self.nLooseBTags[0])
if self.nLooseBTags[1] >= 0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<=' + str(
self.nLooseBTags[1])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<=' + str(
self.nLooseBTags[1])
MC_MuMuMu = "&&".join([
MC_hadronSelection, MuMuMuSelection, "abs(mlmZ_mass-91.2)<10"
])
MC_EEE = "&&".join(
[MC_hadronSelection, EEESelection, "abs(mlmZ_mass-91.2)<10"])
MC_EEMu = "&&".join(
[MC_hadronSelection, EEMuSelection, "abs(mlmZ_mass-91.2)<10"])
MC_MuMuE = "&&".join(
[MC_hadronSelection, MuMuESelection, "abs(mlmZ_mass-91.2)<10"])
MC_3l = "((" + MC_MuMuMu + ")||(" + MC_EEE + ")||(" + MC_EEMu + ")||(" + MC_MuMuE + "))"
data_MuMuMu = "&&".join([
data_hadronSelection, MuMuMuSelection, "abs(mlmZ_mass-91.2)<10"
])
data_EEE = "&&".join(
[data_hadronSelection, EEESelection, "abs(mlmZ_mass-91.2)<10"])
data_EEMu = "&&".join([
data_hadronSelection, EEMuSelection, "abs(mlmZ_mass-91.2)<10"
])
data_MuMuE = "&&".join([
data_hadronSelection, MuMuESelection, "abs(mlmZ_mass-91.2)<10"
])
######yield_MC_3l computed for ALL channels but lumi changes slightly here depending on channel
yield_MC_3l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_MC_looseSelection_3l: %s" % yield_MC_3l
yield_data_MuMuMu = u_float(
getYieldFromChain(setup.sample['Data']['MuMu']['chain'],
cutString=data_MuMuMu,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data_looseSelection_MuMuMu: %s" % yield_data_MuMuMu
yield_data_EEE = u_float(
getYieldFromChain(setup.sample['Data']['EE']['chain'],
cutString=data_EEE,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data_looseSelection_EEE: %s" % yield_data_EEE
yield_data_EMu = u_float(
getYieldFromChain(setup.sample['Data']['EMu']['chain'],
cutString="((" + data_MuMuE + ')||(' +
data_EEMu + '))',
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data_looseSelection_EMu: %s" % yield_data_EMu
yield_data_3l = yield_data_MuMuMu + yield_data_EEE + yield_data_EMu
if setup.verbose: print "yield_data_3l: %s" % yield_data_3l
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets', 'DY', 'other']:
yield_other += setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample[s][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_looseSelection_other %s added, now: %s" % (
s, yield_other)
normRegYield = yield_data_3l - yield_other
if normRegYield.val < 0:
print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n" % (
yield_data_3l, yield_other)
print "normRegYield", normRegYield
print "\n Control Region predictys ", normRegYield, " TTZ events in data; ", yield_MC_3l, " TTZ events in MC. Ratio ---> ", (
normRegYield / yield_MC_3l)
print "DD-TTZ ---> ", (normRegYield / yield_MC_3l) * yield_MC_2l
return (normRegYield / yield_MC_3l) * yield_MC_2l
def _estimate(self, region, channel, setup):
logger.info("Data-driven TTZ estimate for region " + str(region) +
" in channel " + channel + " and setup " + str(setup.sys) +
":")
#Sum of all channels for 'all'
if channel == 'all':
estimate = sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
])
else:
zWindow = 'allZ' if channel == 'EMu' else 'offZ'
preSelection = setup.preselection('MC',
zWindow=zWindow,
channel=channel)
MC_2l = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = setup.weightString()
logger.info("weight: %s", weight)
yield_ttZ_2l = setup.lumi[channel] / 1000. * self.yieldFromCache(
setup, 'TTZ', channel, MC_2l, weight)
logger.info("yield_MC_2l: %s" % yield_ttZ_2l)
if self.useTop16009:
sysError = max((abs(x) for x in self.sysErrTop16009
)) # not sure yet to handle assymetric errors
statError = max((abs(x) for x in self.statErrTop16009))
error = sqrt(sysError * sysError + statError * statError)
return u_float(self.ratioTop16009, error) * yield_ttZ_2l
else:
# pt leptons > 30, 20, 10 GeV
useTrigger = False # setup.parameters['useTriggers'] # better not to use three lepton triggers, seems to be too inefficient
lllSelection = {}
lllSelection['MuMu'] = "&&".join([
getLeptonString(3, 0),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_3mu" if useTrigger else "")
lllSelection['MuMuE'] = "&&".join([
getLeptonString(2, 1),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_2mu1e" if useTrigger else "")
lllSelection['MuEE'] = "&&".join([
getLeptonString(1, 2),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_2e1mu" if useTrigger else "")
lllSelection['EE'] = "&&".join([
getLeptonString(0, 3),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_3e" if useTrigger else "")
lllSelection['EMu'] = "((" + lllSelection[
'MuMuE'] + ")||(" + lllSelection['MuEE'] + "))"
bJetSelectionM = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890))"
bJetSelectionL = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.605))"
zMassSelection = "abs(mlmZ_mass-91.1876)<10"
# Start from base hadronic selection and add loose b-tag and Z-mass requirement
selection = {}
for dataOrMC in ["Data", "MC"]:
selection[dataOrMC] = setup.selection(
dataOrMC,
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
selection[dataOrMC] += "&&" + bJetSelectionL + ">=" + str(
self.nLooseBTags[0])
selection[dataOrMC] += "&&" + zMassSelection
logger.info("Selection " + dataOrMC + ": " +
selection[dataOrMC])
# Calculate yields (take together channels together)
channels = ['MuMu', 'EMu', 'EE']
yield_ttZ_3l = sum(
self.yieldFromCache(
setup, 'TTZ', c, "&&".join(
[lllSelection[c], selection["MC"]]), weight) *
setup.dataLumi[channel] / 1000
for c in ['MuMu', 'EMu', 'EE'])
yield_other = sum(
self.yieldFromCache(
setup, s, c, "&&".join(
[lllSelection[c], selection["MC"]]), weight) *
setup.dataLumi[channel] / 1000
for c in ['MuMu', 'EMu', 'EE']
for s in ['TTJets', 'DY', 'other'])
yield_data_3l = sum(
self.yieldFromCache(
setup, 'Data', c, "&&".join(
[lllSelection[c], selection["Data"]]), "(1)")
for c in ['MuMu', 'EMu', 'EE'])
if not yield_ttZ_3l > 0:
logger.warn("No yield for 3l selection")
estimate = u_float(0, 0)
yield_ttZ_data = yield_data_3l - yield_other
if yield_ttZ_data < 0:
logger.warn("Data-driven ratio is negative!")
yield_ttZ_data = u_float(0, 0)
logger.info("Control region predictions: ")
logger.info(" data: " + str(yield_data_3l))
logger.info(" MC other: " + str(yield_other))
logger.info(" TTZ (MC): " + str(yield_ttZ_3l))
logger.info(" TTZ (data): " + str(yield_ttZ_data))
logger.info(" TTZ (ratio): " +
str(yield_ttZ_data / yield_ttZ_3l))
estimate = (yield_ttZ_data / yield_ttZ_3l) * yield_ttZ_2l
logger.info(" --> " + str(estimate))
return estimate
def _estimate(self, region, channel, setup):
#Sum of all channels for 'all'
if channel == 'all':
estimate = sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
])
#MC based for 'EMu'
elif channel == 'EMu':
weight = setup.weightString()
preSelection = setup.preselection('MC',
zWindow='allZ',
channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
estimate = setup.lumi[channel] / 1000. * u_float(
**setup.sample['DY'][channel].getYieldFromDraw(
selectionString=cut, weightString=weight))
#Data driven for EE and MuMu (calculate for data luminosity)
else:
weight = setup.weightString()
cut_offZ_1b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='offZ',
**setup.defaultParameters(update={'nBTags': (1,
-1)}))['cut']
])
cut_onZ_0b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (0,
0)}))['cut']
])
cut_data_onZ_0b = "&&".join([
region.cutString(),
setup.selection(
'Data',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (0,
0)}))['cut']
])
# Calculate ratio (offZ,1b)/(onZ,0b)
yield_offZ_1b = u_float(
**setup.sample['DY'][channel].getYieldFromDraw(
selectionString=cut_offZ_1b,
weightString=weight)) * setup.dataLumi[channel] / 1000
yield_onZ_0b = u_float(
**setup.sample['DY'][channel].getYieldFromDraw(
selectionString=cut_onZ_0b,
weightString=weight)) * setup.dataLumi[channel] / 1000
R = yield_offZ_1b / yield_onZ_0b if yield_onZ_0b > 0 else 0
# Calculate data-other onZ for 0 b-jets region
yield_data = u_float(
**setup.sample['Data'][channel].getYieldFromDraw(
selectionString=cut_data_onZ_0b, weightString="(1)"))
yield_other = sum(
u_float(**setup.sample[s][channel].getYieldFromDraw(
selectionString=cut_onZ_0b, weightString=weight)) for s in
['TTJets', 'TTZ', 'other']) * setup.dataLumi[channel] / 1000
normRegYield = yield_data - yield_other
# Calculate DY estimate in 1 b-jet region (and scale back to MC lumi)
estimate = R * normRegYield * setup.lumi[channel] / setup.dataLumi[
channel]
logger.info("Calculating data-driven DY estimate in channel " +
channel + " using lumi " +
str(setup.dataLumi[channel]) + ":")
logger.info("yield DY offZ/1b: " + str(yield_offZ_1b))
logger.info("yield DY onZ/0b: " + str(yield_onZ_0b))
logger.info("R: " + str(R))
logger.info("yield data onZ/0b: " + str(yield_data))
logger.info("yield other onZ/0b: " + str(yield_other))
logger.info("yield (data-other) onZ/0b: " + str(normRegYield))
logger.info("yield expected DY onZ/1b: " + str(normRegYield * R))
if normRegYield < 0 and yield_data > 0:
logger.warn("Negative normalization region yield!")
logger.info('Estimate for DY in ' + channel + ' channel (lumi=' +
str(setup.lumi[channel]) + '/pb): ' + str(estimate) +
(" (negative estimated being replaced by 0)"
if estimate < 0 else ""))
return estimate if estimate > 0 else u_float(0, 0)
def _estimate(self, region, channel, setup):
#Sum of all channels for 'all'
if channel == 'all':
return sum([
self.cachedEstimate(region, c, channel, setup)
for c in ['MuMu', 'EE', 'EMu']
])
else:
#Data driven for EE, EMu and MuMu.
zWindow = 'allZ' if channel == 'EMu' else 'offZ'
preSelection = setup.preselection('MC',
zWindow=zWindow,
channel=channel)
#check lumi consistency
assert abs(
1. -
setup.lumi[channel] / setup.sample['Data'][channel]['lumi']
) < 0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s" % (
setup.lumi[channel], setup.sample['Data'][channel]['lumi'],
channel)
MC_2l = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
logger.debug("weight: %s", weight)
yield_MC_2l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=selection_MC_2l,
weight=weight,
returnError=True))
if setup.verbose: print "yield_MC_2l: %s" % yield_MC_2l
# pt leptons > 30, 20, 10 GeV
useTrigger = False # setup.parameters['useTriggers'] # better not to use three lepton triggers, seems to be too inefficient
mumumuSelection = "&&".join([
getLeptonString(3, 0),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_3mu" if useTrigger else "")
mumueSelection = "&&".join([
getLeptonString(2, 1),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_2mu1e" if useTrigger else "")
mueeSelection = "&&".join([
getLeptonString(1, 2),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_2e1mu" if useTrigger else "")
eeeSelection = "&&".join([
getLeptonString(0, 3),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_3e" if useTrigger else "")
lllSelection = "((" + ")||(".join([
mumumuSelection, mumueSelection, mueeSelection, eeeSelection
]) + "))"
bJetSelectionM = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890))"
bJetSelectionL = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.605))"
zMassSelection = "abs(mlmZ_mass-91.1876)<10"
# Start from base hadronic selection and add loose b-tag and Z-mass requirement
selection = {}
for dataOrMC in ["Data", "MC"]:
selection[dataOrMC] = setup.selection(
dataOrMC,
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
selection[dataOrMC] += bJetSelectionL + ">=" + str(
self.nLooseBTags[0])
selection[dataOrMC] += zMassSelection
MC_3l = lllSelection + "&&" + selection["MC"]
data_mumumu = mumumuSelection + "&&" + selection["Data"]
data_mumue = mumueSelection + "&&" + selection["Data"]
data_muee = mueeSelection + "&&" + selection["Data"]
data_eee = eeeSelection + "&&" + selection["Data"]
# Calculate yields (take together)
yield_ttZ_2l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=MC_2l,
weight=weight,
returnError=True))
yield_ttZ_3l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
yield_data_mumumu = u_float(
getYieldFromChain(setup.sample['Data']['MuMu']['chain'],
cutString=data_mumumu,
weight=weight,
returnError=True))
yield_data_eee = u_float(
getYieldFromChain(setup.sample['Data']['EE']['chain'],
cutString=data_eee,
weight=weight,
returnError=True))
yield_data_mue = u_float(
getYieldFromChain(setup.sample['Data']['EMu']['chain'],
cutString="((" + data_mumue + ')||(' +
data_muee + '))',
weight=weight,
returnError=True))
yield_data_3l = yield_data_mumumu + yield_data_mue + yield_data_eee
#electroweak subtraction
yield_other = u_float(0., 0.)
for s in ['TTJets', 'DY', 'other']:
yield_other += setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample[s][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
yield_ttZ_data = yield_data_3l - yield_other
if normRegYield.val < 0:
logger.warn("Data-driven estimate is negative!")
logger.info("Control region predictions: ")
logger.info(" data: " + str(yield_data_3l))
logger.info(" MC other: " + str(yield_other))
logger.info(" TTZ (MC): " + str(yield_ttZ_3l))
logger.info(" TTZ (data): " + str(yield_ttZ_data))
logger.info(" TTZ (ratio): " + str(yield_ttZ_data / yield_ttZ_3l))
return (yield_ttZ_data / yield_ttZ_3l) * yield_MC_2l
def _estimate(self, region, channel, setup):
logger.info("Data-driven TTZ estimate for region " + str(region) + " in channel " + channel + " and setup " + str(setup.sys) + ":")
#Sum of all channels for 'all'
if channel=='all':
estimate = sum([self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu']])
else:
zWindow= 'allZ' if channel=='EMu' else 'offZ'
preSelection = setup.preselection('MC', zWindow=zWindow, channel=channel)
MC_2l = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = setup.weightString()
logger.info("weight: %s", weight)
yield_ttZ_2l = setup.lumi[channel]/1000.*self.yieldFromCache(setup, 'TTZ', channel, MC_2l, weight)
logger.info("yield_MC_2l: %s"%yield_ttZ_2l)
if self.useTop16009:
sysError = max((abs(x) for x in self.sysErrTop16009)) # not sure yet to handle assymetric errors
statError = max((abs(x) for x in self.statErrTop16009))
error = sqrt(sysError*sysError+statError*statError)
return u_float(self.ratioTop16009, error)*yield_ttZ_2l
else:
# pt leptons > 30, 20, 10 GeV
useTrigger = False # setup.parameters['useTriggers'] # better not to use three lepton triggers, seems to be too inefficient
lllSelection = {}
lllSelection['MuMu'] = "&&".join([getLeptonString(3, 0), getPtThresholdString(30, 20, 10)]) + ("&&HLT_3mu" if useTrigger else "")
lllSelection['MuMuE'] = "&&".join([getLeptonString(2, 1), getPtThresholdString(30, 20, 10)]) + ("&&HLT_2mu1e" if useTrigger else "")
lllSelection['MuEE'] = "&&".join([getLeptonString(1, 2), getPtThresholdString(30, 20, 10)]) + ("&&HLT_2e1mu" if useTrigger else "")
lllSelection['EE'] = "&&".join([getLeptonString(0, 3), getPtThresholdString(30, 20, 10)]) + ("&&HLT_3e" if useTrigger else "")
lllSelection['EMu'] = "(("+lllSelection['MuMuE']+")||("+lllSelection['MuEE']+"))"
bJetSelectionM = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890))"
bJetSelectionL = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.605))"
zMassSelection = "abs(mlmZ_mass-91.1876)<10"
# Start from base hadronic selection and add loose b-tag and Z-mass requirement
selection = {}
for dataOrMC in ["Data", "MC"]:
selection[dataOrMC] = setup.selection(dataOrMC, hadronicSelection = True, **setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. }))['cut']
selection[dataOrMC] += "&&" + bJetSelectionL+">="+str(self.nLooseBTags[0])
selection[dataOrMC] += "&&" + zMassSelection
logger.info("Selection " + dataOrMC + ": " + selection[dataOrMC])
# Calculate yields (take together channels together)
channels = ['MuMu','EMu','EE']
yield_ttZ_3l = sum(self.yieldFromCache(setup, 'TTZ', c, "&&".join([lllSelection[c], selection["MC"]]), weight)*setup.dataLumi[channel]/1000 for c in ['MuMu','EMu','EE'])
yield_other = sum(self.yieldFromCache(setup, s, c, "&&".join([lllSelection[c], selection["MC"]]), weight)*setup.dataLumi[channel]/1000 for c in ['MuMu','EMu','EE'] for s in ['TTJets', 'DY', 'other'])
yield_data_3l = sum(self.yieldFromCache(setup, 'Data', c, "&&".join([lllSelection[c], selection["Data"]]), "(1)") for c in ['MuMu','EMu','EE'])
if not yield_ttZ_3l > 0:
logger.warn("No yield for 3l selection")
estimate = u_float(0, 0)
yield_ttZ_data = yield_data_3l - yield_other
if yield_ttZ_data < 0:
logger.warn("Data-driven ratio is negative!")
yield_ttZ_data = u_float(0, 0)
logger.info("Control region predictions: ")
logger.info(" data: " + str(yield_data_3l))
logger.info(" MC other: " + str(yield_other))
logger.info(" TTZ (MC): " + str(yield_ttZ_3l))
logger.info(" TTZ (data): " + str(yield_ttZ_data))
logger.info(" TTZ (ratio): " + str(yield_ttZ_data/yield_ttZ_3l))
estimate = (yield_ttZ_data/yield_ttZ_3l)*yield_ttZ_2l
logger.info(" --> " + str(estimate))
return estimate
def _estimate(self, region, channel, setup):
#Sum of all channels for 'all'
if channel=='all':
return sum( [ self.cachedEstimate(region, c, channel, setup) for c in ['MuMu', 'EE', 'EMu'] ] )
else:
#Data driven for EE, EMu and MuMu.
zWindow= 'allZ' if channel=='EMu' else 'offZ'
preSelection = setup.preselection('MC', zWindow=zWindow, channel=channel)
#check lumi consistency
assert abs(1.-setup.lumi[channel]/setup.sample['Data'][channel]['lumi'])<0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s"%(setup.lumi[channel], setup.sample['Data'][channel]['lumi'], channel)
MC_2l = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
logger.debug("weight: %s", weight)
yield_MC_2l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = selection_MC_2l, weight=weight, returnError = True) )
if setup.verbose: print "yield_MC_2l: %s"%yield_MC_2l
# pt leptons > 30, 20, 10 GeV
useTrigger = False # setup.parameters['useTriggers'] # better not to use three lepton triggers, seems to be too inefficient
mumumuSelection = "&&".join([getLeptonString(3, 0), getPtThresholdString(30, 20, 10)]) + ("&&HLT_3mu" if useTrigger else "")
mumueSelection = "&&".join([getLeptonString(2, 1), getPtThresholdString(30, 20, 10)]) + ("&&HLT_2mu1e" if useTrigger else "")
mueeSelection = "&&".join([getLeptonString(1, 2), getPtThresholdString(30, 20, 10)]) + ("&&HLT_2e1mu" if useTrigger else "")
eeeSelection = "&&".join([getLeptonString(0, 3), getPtThresholdString(30, 20, 10)]) + ("&&HLT_3e" if useTrigger else "")
lllSelection = "((" + ")||(".join([mumumuSelection, mumueSelection, mueeSelection, eeeSelection]) + "))"
bJetSelectionM = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890))"
bJetSelectionL = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.605))"
zMassSelection = "abs(mlmZ_mass-91.1876)<10"
# Start from base hadronic selection and add loose b-tag and Z-mass requirement
selection = {}
for dataOrMC in ["Data", "MC"]:
selection[dataOrMC] = setup.selection(dataOrMC, hadronicSelection = True, **setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. }))['cut']
selection[dataOrMC] += bJetSelectionL+">="+str(self.nLooseBTags[0])
selection[dataOrMC] += zMassSelection
MC_3l = lllSelection + "&&" + selection["MC"]
data_mumumu = mumumuSelection + "&&" + selection["Data"]
data_mumue = mumueSelection + "&&" + selection["Data"]
data_muee = mueeSelection + "&&" + selection["Data"]
data_eee = eeeSelection + "&&" + selection["Data"]
# Calculate yields (take together)
yield_ttZ_2l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = MC_2l, weight=weight, returnError = True))
yield_ttZ_3l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = MC_3l, weight=weight, returnError = True))
yield_data_mumumu = u_float(getYieldFromChain(setup.sample['Data']['MuMu']['chain'], cutString = data_mumumu, weight=weight, returnError = True))
yield_data_eee = u_float(getYieldFromChain(setup.sample['Data']['EE']['chain'], cutString = data_eee, weight=weight, returnError = True))
yield_data_mue = u_float(getYieldFromChain(setup.sample['Data']['EMu']['chain'], cutString = "(("+data_mumue+')||('+data_muee+'))', weight=weight, returnError = True))
yield_data_3l = yield_data_mumumu + yield_data_mue + yield_data_eee
#electroweak subtraction
yield_other = u_float(0., 0.)
for s in ['TTJets' , 'DY', 'other']:
yield_other+= setup.lumi[channel]/1000.* u_float(getYieldFromChain(setup.sample[s][channel]['chain'], cutString = MC_3l, weight=weight, returnError=True))
yield_ttZ_data = yield_data_3l - yield_other
if normRegYield.val<0: logger.warn("Data-driven estimate is negative!")
logger.info("Control region predictions: ")
logger.info(" data: " + str(yield_data_3l))
logger.info(" MC other: " + str(yield_other))
logger.info(" TTZ (MC): " + str(yield_ttZ_3l))
logger.info(" TTZ (data): " + str(yield_ttZ_data))
logger.info(" TTZ (ratio): " + str(yield_ttZ_data/yield_ttZ_3l))
return (yield_ttZ_data/yield_ttZ_3l)*yield_MC_2l
