from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
_N = [1, 2]
_leadingDiscr = [("T", 0.898), ("M", 0.679), ("L", 0.244)]
_subLeadingDiscr = _leadingDiscr[1:]
scenarios = []
_subScen = []
for leadName, leadCut in _leadingDiscr:
scenarios.append(
Scenario("N1discr" + leadName,
jetNumber=1,
jetNumberCutDirection="GEQ",
leadingDiscriminatorCut=leadCut))
for subName, subCut in _subLeadingDiscr:
if subCut > leadCut:
continue
_subScen.append(
Scenario("N2discr" + leadName + "subdiscr" + subName,
jetNumber=2,
jetNumberCutDirection="GEQ",
leadingDiscriminatorCut=leadCut,
subleadingDiscriminatorCut=subCut))
scenarios.extend(_subScen)
optimisation.addBTagVariations(scenarios)
# Perform the signal analysis with the JES variations in addition to # the "golden" analysis doJESVariation = False # Perform the signal analysis with the PU weight variations # https://twiki.cern.ch/twiki/bin/view/CMS/PileupSystematicErrors doPUWeightVariation = False # Do variations for optimisation # Note: Keep number of variations below 200 to keep file sizes reasonable # Note: Currently it is not possible to vary the tau selection -related variables, because only one JES and MET producer is made (tau selection influences type I MET correction and JES) doOptimisation = False from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme myOptimisation = HPlusOptimisationScheme() #myOptimisation.addTauPtVariation([40.0, 50.0]) #myOptimisation.addTauIsolationVariation([]) #myOptimisation.addTauIsolationContinuousVariation([]) #myOptimisation.addRtauVariation([0.0, 0.7]) #myOptimisation.addJetNumberSelectionVariation(["GEQ3", "GEQ4"]) #myOptimisation.addJetEtVariation([20.0, 30.0]) #myOptimisation.addJetBetaVariation(["GT0.0","GT0.5","GT0.7"]) #myOptimisation.addMETSelectionVariation([50.0, 60.0, 70.0]) #myOptimisation.addBJetLeadingDiscriminatorVariation([0.898, 0.679]) #myOptimisation.addBJetSubLeadingDiscriminatorVariation([0.679, 0.244]) #myOptimisation.addBJetEtVariation([]) #myOptimisation.addBJetNumberVariation(["GEQ1", "GEQ2"]) #myOptimisation.addDeltaPhiVariation([180.0,160.0,140.0]) #myOptimisation.addTopRecoVariation(["None","chi"]) # Valid options: None, chi, std, Wselection myOptimisation.disableMaxVariations()
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme # Note: Keep number of variations below 200 to keep file sizes reasonable # Note: Currently it is not possible to vary the tau selection -related variables, because only one JES and MET producer is made (tau selection influences type I MET correction and JES) # There must be 'optimisation' object optimisation = HPlusOptimisationScheme() #optimisation.printOptions() # Uncomment to find out the implemented methods #optimisation.addTauPtVariation([40.0, 50.0, 60.0, 70., 80.]) #optimisation.addTauIsolationVariation([]) #optimisation.addTauIsolationContinuousVariation([]) #optimisation.addRtauVariation([0.0, 0.7, 0.8]) #optimisation.addJetNumberSelectionVariation(["GEQ3", "GEQ4"]) #optimisation.addJetEtVariation([20.0, 30.0]) #optimisation.addJetBetaVariation(["GT0.0","GT0.5","GT0.7"]) #optimisation.addMETSelectionVariation([60.0, 70.0, 80.0, 90.,100.0]) #optimisation.addBJetLeadingDiscriminatorVariation([0.898, 0.679]) #optimisation.addBJetSubLeadingDiscriminatorVariation([0.679, 0.244]) #optimisation.addBJetEtVariation([]) #optimisation.addBJetNumberVariation(["GEQ1", "GEQ2"]) #import btagScenarios # Pick btag scan points #optimisation.addBTagVariations(btagScenarios.scenarios) #optimisation.addDeltaPhiVariation([180.0,170.0,160.0,150.0]) optimisation.addTopRecoVariation(["None","chi","std","Wselection","Bselection"]) # Valid options: None, chi, std, Wselection, Bselection #import invMassScenarios # Pick invariant mass scan points #optimisation.addInvariantMassVariation(invMassScenarios.scenarios)
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme
# Note: Keep number of variations below 200 to keep file sizes reasonable
# Note: Currently it is not possible to vary the tau selection -related variables, because only one JES and MET producer is made (tau selection influences type I MET correction and JES)
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
#optimisation.printOptions() # Uncomment to find out the implemented methods
optimisation.addTopRecoVariation(
["None", "chi", "Wselection",
"Bselection"]) # Valid options: None, chi, std, Wselection, Bselection
#optimisation.addTopRecoVariation(["chi"])
#optimisation.addTopRecoVariation(["None","chi","Bselection"])
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
_N = [1, 2]
_leadingDiscr = [("T", 0.898), ("M", 0.679), ("L", 0.244)]
_subLeadingDiscr = _leadingDiscr[1:]
scenarios = []
_subScen = []
# Symmetric cuts for at least 1 b jet
for leadName,leadCut in _leadingDiscr:
scenarios.append(Scenario("N1discr"+leadName,
jetNumber = 1,
jetNumberCutDirection = "GEQ",
leadingDiscriminatorCut = leadCut))
# Symmetric cuts for at least 1 b jet
for leadName,leadCut in _leadingDiscr:
if leadName in ["L","M"]:
scenarios.append(Scenario("N2discr"+leadName,
jetNumber = 2,
jetNumberCutDirection = "GEQ",
leadingDiscriminatorCut = leadCut))
scenarios.extend(_subScen)
optimisation.addBTagVariations(scenarios)
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
_N = [3, 4]
_pt = [20, 25, 30]
scenarios = []
_subScen = []
for n in _N:
for pt in _pt:
scenarios.append(Scenario("N%dpt%d"%(n,pt),
jetNumber = n,
jetNumberCutDirection = "GEQ",
ptCut = pt))
scenarios.extend(_subScen)
optimisation.addJetVariations(scenarios)
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
_neutrinoPzSolutionSelectionMethods = ["DeltaEtaMax", "Smaller"]
scenarios = []
for currentPzSelectionMethod in _neutrinoPzSolutionSelectionMethods:
scenarios.extend([
Scenario("PzSelection"+currentPzSelectionMethod+"TopInvMassCutNone",
topInvMassLowerCut = -1, # negative value means no cut
topInvMassUpperCut = -1, # negative value means no cut
pzSelectionMethod = currentPzSelectionMethod
),
Scenario("PzSelection"+currentPzSelectionMethod+"TopInvMassCutLoose",
topInvMassLowerCut = 100, # negative value means no cut
topInvMassUpperCut = 240, # negative value means no cut
pzSelectionMethod = currentPzSelectionMethod
),
Scenario("PzSelection"+currentPzSelectionMethod+"TopInvMassCutMedium",
topInvMassLowerCut = 140, # negative value means no cut
topInvMassUpperCut = 200, # negative value means no cut
pzSelectionMethod = currentPzSelectionMethod
),
Scenario("PzSelection"+currentPzSelectionMethod+"TopInvMassCutTight",
topInvMassLowerCut = 157, # negative value means no cut
topInvMassUpperCut = 187, # negative value means no cut
pzSelectionMethod = currentPzSelectionMethod
),
])
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
_neutrinoPzSolutionSelectionMethods = ["DeltaEtaMax", "Smaller"]
scenarios = []
for currentPzSelectionMethod in _neutrinoPzSolutionSelectionMethods:
scenarios.extend([
Scenario(
"PzSelection" + currentPzSelectionMethod + "TopInvMassCutNone",
topInvMassLowerCut=-1, # negative value means no cut
topInvMassUpperCut=-1, # negative value means no cut
pzSelectionMethod=currentPzSelectionMethod),
Scenario(
"PzSelection" + currentPzSelectionMethod + "TopInvMassCutLoose",
topInvMassLowerCut=100, # negative value means no cut
topInvMassUpperCut=240, # negative value means no cut
pzSelectionMethod=currentPzSelectionMethod),
Scenario(
"PzSelection" + currentPzSelectionMethod + "TopInvMassCutMedium",
topInvMassLowerCut=140, # negative value means no cut
topInvMassUpperCut=200, # negative value means no cut
pzSelectionMethod=currentPzSelectionMethod),
Scenario(
"PzSelection" + currentPzSelectionMethod + "TopInvMassCutTight",
topInvMassLowerCut=157, # negative value means no cut
topInvMassUpperCut=187, # negative value means no cut
pzSelectionMethod=currentPzSelectionMethod),
])
# Perform the signal analysis with the JES variations in addition to # the "golden" analysis doJESVariation = False # Perform the signal analysis with the PU weight variations # https://twiki.cern.ch/twiki/bin/view/CMS/PileupSystematicErrors doPUWeightVariation = False # Do variations for optimisation # Note: Keep number of variations below 200 to keep file sizes reasonable # Note: Currently it is not possible to vary the tau selection -related variables, because only one JES and MET producer is made (tau selection influences type I MET correction and JES) doOptimisation = False from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme myOptimisation = HPlusOptimisationScheme() #myOptimisation.addTauPtVariation([40.0, 50.0]) #myOptimisation.addTauIsolationVariation([]) #myOptimisation.addTauIsolationContinuousVariation([]) #myOptimisation.addRtauVariation([0.0, 0.7]) #myOptimisation.addJetNumberSelectionVariation(["GEQ3", "GEQ4"]) #myOptimisation.addJetEtVariation([20.0, 30.0]) #myOptimisation.addJetBetaVariation(["GT0.0","GT0.5","GT0.7"]) #myOptimisation.addMETSelectionVariation([50.0, 60.0, 70.0]) #myOptimisation.addBJetLeadingDiscriminatorVariation([0.898, 0.679]) #myOptimisation.addBJetSubLeadingDiscriminatorVariation([0.679, 0.244]) #myOptimisation.addBJetEtVariation([]) #myOptimisation.addBJetNumberVariation(["GEQ1", "GEQ2"]) #myOptimisation.addDeltaPhiVariation([180.0,160.0,140.0]) #myOptimisation.addTopRecoVariation(["None","chi"]) # Valid options: None, chi, std, Wselection myOptimisation.disableMaxVariations()
from HiggsAnalysis.HeavyChHiggsToTauNu.OptimisationScheme import HPlusOptimisationScheme, Scenario
# There must be 'optimisation' object
optimisation = HPlusOptimisationScheme()
_pt = [50,60,70,80,90]
scenarios = []
for pt in _pt:
scenarios.append(Scenario("%d"%(pt),
METCut = pt))
optimisation.addMETVariations(scenarios)