#!/usr/bin/env python3
##########################################################################
# basf2 (Belle II Analysis Software Framework) #
# Author: The Belle II Collaboration #
# #
# See git log for contributors and copyright holders. #
# This file is licensed under LGPL-3.0, see LICENSE.md. #
##########################################################################
""" Skim list building functions for systematics studies """
__authors__ = [
"Sam Cunliffe",
"Torben Ferber",
"Ilya Komarov",
"Yuji Kato"
]
import basf2 as b2
import modularAnalysis as ma
import vertex
from skim import BaseSkim, CombinedSkim, fancy_skim_header
from stdCharged import stdE, stdK, stdMu, stdPi, stdPr
from stdPhotons import stdPhotons
from stdPi0s import stdPi0s
from stdV0s import stdKshorts, stdLambdas
from variables import variables as vm
# TODO: Add liaison name and email address
__liaison__ = ""
__liaison_leptonID__ = "Marcel Hohmann"
_VALIDATION_SAMPLE = "mdst14.root"
[docs]
@fancy_skim_header
class SystematicsDstar(BaseSkim):
"""
Primarily used for hadron and lepton ID studies.
Lists in this skim are those defined in `PiKFromDstarList`.
"""
__authors__ = ["Sam Cunliffe", "Torben Ferber", "Ilya Komarov", "Yuji Kato", "Racha Cheaib"]
__description__ = ""
__contact__ = __liaison__
__category__ = "systematics"
ApplyHLTHadronCut = True
[docs]
def load_standard_lists(self, path):
stdK("all", path=path)
stdPi("all", path=path)
TestSampleProcess = "ccbar"
[docs]
def build_lists(self, path):
return self.PiKFromDstarList(path)
[docs]
def PiKFromDstarList(self, path):
"""Build PiKFromDstarList lists for systematics skims."""
D0Cuts = "1.75 < M < 2.0"
DstarCuts = "massDifference(0)<0.16 and useCMSFrame(p) > 1.5"
ma.cutAndCopyList("K-:syst", "K-:all", "dr<2 and abs(dz)<4", path=path)
ma.cutAndCopyList("pi+:syst", "pi+:all", "dr<2 and abs(dz)<4", path=path)
D0Channel = ["K-:syst pi+:syst"]
D0List = []
for chID, channel in enumerate(D0Channel):
ma.reconstructDecay(f"D0:syst{chID} -> {channel}", D0Cuts, chID, path=path)
D0List.append(f"D0:syst{chID}")
DstarChannel = []
for channel in D0List:
DstarChannel.append(f"{channel} pi+:syst")
DstarList = []
for chID, channel in enumerate(DstarChannel):
ma.reconstructDecay(f"D*+:syst{chID} -> {channel}", DstarCuts, chID, path=path)
DstarList.append(f"D*+:syst{chID}")
return DstarList
[docs]
@fancy_skim_header
class SystematicsTracking(BaseSkim):
"""
Lists in this skim are those defined in `BtoDStarPiList` and `DstarToD0PiPartList`.
"""
__authors__ = ["Sam Cunliffe", "Torben Ferber", "Ilya Komarov", "Yuji Kato"]
__description__ = ""
__contact__ = __liaison__
__category__ = "systematics"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdK("loose", path=path)
stdPi("loose", path=path)
stdPi0s("eff40_May2020", path=path)
[docs]
def build_lists(self, path):
return self.BtoDStarPiList(path) + self.DstarToD0PiPartList(path)
[docs]
def BtoDStarPiList(self, path):
"""Build BtoDStarPiList lists for systematics skims."""
D0Cuts = "1.835 < M < 1.895"
DstarCuts = "massDifference(0)<0.16"
B0Cuts = "Mbc > 5.2 and abs(deltaE) < 0.3"
# D0
D0Channel = ["K+:loose pi-:loose", "K+:loose pi-:loose pi-:loose pi+:loose", "K+:loose pi-:loose pi0:eff40_May2020"]
D0List = []
for chID, channel in enumerate(D0Channel):
resonanceName = "anti-D0:loose" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, D0Cuts, chID, path=path)
# vertex.raveFit(resonanceName, 0.0, path=path)
ma.copyLists("anti-D0:loose", ["anti-D0:loose0", "anti-D0:loose1", "anti-D0:loose2"], path=path)
D0List.append("anti-D0:loose")
# Dstar
DstarChannel = []
for channel in D0List:
DstarChannel.append(channel + " pi-:loose")
DstarList = []
for chID, channel in enumerate(DstarChannel):
resonanceName = "D*-:loose" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, DstarCuts, chID, path=path)
# vertex.raveFit(resonanceName, 0.0)
DstarList.append(resonanceName)
# B0
B0Channel = []
for channel in DstarList:
B0Channel.append(channel + " pi+:loose")
B0List = []
for chID, channel in enumerate(B0Channel):
resonanceName = "B0:sys" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, B0Cuts, chID, path=path)
B0List.append(resonanceName)
# vertex.raveFit(resonanceName, 0.0)
return B0List
[docs]
def DstarToD0PiPartList(self, path):
"""Build DstarToD0PiPartList lists for systematics skims."""
ma.fillParticleList("pi+:fromks", "chiProb > 0.001 and pionID > 0.1 and dr > 0.1", path=path)
# D-
DminusCuts = "1.0 < M < 1.75"
DminusChannel = ["pi-:fromks pi+:loose pi-:loose"]
for chID, channel in enumerate(DminusChannel):
resonanceName = "D-:loose" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, DminusCuts, chID, path=path)
# Dstar
DstarCuts = "massDifference(0)<0.2 and useCMSFrame(p) > 2.0"
DstarChannel = []
DstarChannel.append("D-:loose0" + " pi+:loose")
DstarList = []
for chID, channel in enumerate(DstarChannel):
resonanceName = "D*0:loose" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, DstarCuts, chID, path=path)
DstarList.append(resonanceName)
return DstarList
[docs]
@fancy_skim_header
class Resonance(BaseSkim):
"""
Lists in this skim are those defined in `getDsList`, `getDstarList`,
`getSigmacList`, `getmumugList`, `getBZeroList`, and `getBPlusList`.
"""
__authors__ = ["Sam Cunliffe", "Torben Ferber", "Ilya Komarov", "Yuji Kato"]
__description__ = ""
__contact__ = __liaison__
__category__ = "systematics"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdK("loose", path=path)
stdMu("loose", path=path)
stdPi("loose", path=path)
stdPr("loose", path=path)
stdPi0s("eff40_May2020Fit", path=path)
[docs]
def build_lists(self, path):
return (
self.getDsList(path)
+ self.getDstarList(path)
+ self.getSigmacList(path)
+ self.getmumugList(path)
+ self.getBZeroList(path)
+ self.getBPlusList(path)
)
[docs]
def getDsList(self, path):
"""Build Ds list for systematics skims."""
DsCuts = "1.90 < M < 2.04"
ma.reconstructDecay("phi:res -> K+:loose K-:loose", "1.01 < M < 1.03", path=path)
ma.reconstructDecay("K*0:res -> K+:loose pi-:loose", "0.7 < M < 1.1", path=path)
DsChannel = ["phi:res pi+:loose"]
DsList = []
for chID, channel in enumerate(DsChannel):
particlename = f"D_s+:Resonance{int(chID)}"
ma.reconstructDecay(particlename + " -> " + channel, DsCuts, chID, path=path)
DsList.append(particlename)
return DsList
[docs]
def getDstarList(self, path):
"""Build Dstar list for systematics skims."""
DplusCuts = "1.8 < M < 1.93"
DstarCuts = "massDifference(0)<0.16 and useCMSFrame(p)>2.0"
DplusChannel = ["K-:loose pi+:loose pi+:loose"]
DplusList = []
for chID, channel in enumerate(DplusChannel):
ma.reconstructDecay("D+:resonance" + str(chID) + " -> " + channel, DplusCuts, chID, path=path)
vertex.raveFit("D+:resonance" + str(chID), 0.0, path=path)
DplusList.append("D+:resonance" + str(chID))
DstarChannel = []
for channel in DplusList:
DstarChannel.append(channel + " pi0:eff40_May2020")
DstarList = []
for chID, channel in enumerate(DstarChannel):
ma.reconstructDecay("D*+:resonance" + str(chID) + " -> " + channel, DstarCuts, chID, path=path)
DstarList.append("D*+:resonance" + str(chID))
return DstarList
[docs]
def getSigmacList(self, path):
"""Build Sigmac list for systematics skims."""
LambdacCuts = "2.24 < M < 2.33"
SigmacCuts = "massDifference(0)<0.28 and useCMSFrame(p) > 2.5"
LambdacChannel = ["p+:loose K-:loose pi+:loose"]
LambdacList = []
for chID, channel in enumerate(LambdacChannel):
ma.reconstructDecay("Lambda_c+:resonance" + str(chID) + " -> " + channel, LambdacCuts, chID, path=path)
vertex.raveFit("Lambda_c+:resonance" + str(chID), 0.0, path=path)
LambdacList.append("Lambda_c+:resonance" + str(chID))
SigmacList = []
SigmacPlusChannel = []
# Sigma_c++
for channel in LambdacList:
SigmacPlusChannel.append(channel + " pi+:loose")
for chID, channel in enumerate(SigmacPlusChannel):
ma.reconstructDecay("Sigma_c++:resonance" + str(chID) + " -> " + channel, SigmacCuts, chID, path=path)
SigmacList.append("Sigma_c++:resonance" + str(chID))
# Sigma_c0
Sigmac0Channel = []
for channel in LambdacList:
Sigmac0Channel.append(channel + " pi-:loose")
Sigmac0List = []
for chID, channel in enumerate(Sigmac0Channel):
ma.reconstructDecay("Sigma_c0:resonance" + str(chID) + " -> " + channel, SigmacCuts, chID, path=path)
Sigmac0List.append("Sigma_c0:resonance" + str(chID))
return SigmacList
[docs]
def getmumugList(self, path):
"""Build mumug list for systematics skims."""
vphoChannel = ["mu+:loose mu-:loose"]
vphocuts = ""
vphoList = []
for chID, channel in enumerate(vphoChannel):
resonanceName = "vpho:resonance" + str(chID)
ma.reconstructDecay("vpho:resonance" + str(chID) + " -> " + channel, vphocuts, chID, path=path)
ma.applyCuts(resonanceName, "nTracks == 2 and M < formula(Ecms*0.9877)", path=path)
vertex.raveFit(resonanceName, 0.0, path=path)
ma.applyCuts(resonanceName, "M < formula(Ecms*0.9877)", path=path)
vphoList.append(resonanceName)
return vphoList
[docs]
def getBZeroList(self, path):
"""Build BZero list for systematics skims."""
BZeroCuts = "Mbc > 5.2 and abs(deltaE) < 0.3"
BZeroChannel = ["D-:resonance0 pi+:loose"]
BZeroList = []
for chID, channel in enumerate(BZeroChannel):
resonanceName = "B0:resonance" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, BZeroCuts, chID, path=path)
BZeroList.append(resonanceName)
return BZeroList
[docs]
def getBPlusList(self, path):
"""Build Bplus list for systematics skims."""
antiDZeroCut = "1.82 < M < 1.90"
antiDZeroChannel = ["K+:loose pi-:loose"]
antiDZeroList = []
for chID, channel in enumerate(antiDZeroChannel):
resonanceName = "anti-D0:resonance" + str(chID)
ma.reconstructDecay(resonanceName + " -> " + channel, antiDZeroCut, chID, path=path)
vertex.raveFit(resonanceName, 0.0, path=path)
antiDZeroList.append(resonanceName)
BPlusChannel = []
for channel in antiDZeroList:
BPlusChannel.append(channel + " pi+:loose")
BPlusCuts = "Mbc > 5.2 and abs(deltaE) < 0.3"
BPlusList = []
for chID, channel in enumerate(BPlusChannel):
ma.reconstructDecay("B+:resonance" + str(chID) + " -> " + channel, BPlusCuts, chID, path=path)
BPlusList.append("B+:resonance" + str(chID))
return BPlusList
[docs]
@fancy_skim_header
class SystematicsRadMuMu(BaseSkim):
"""
We require one cluster-matched electron (the other is not required to match a
cluster). No selection on the photon as the sample must be unbiased.
"""
__authors__ = ["Torben Ferber"]
__description__ = (
"Skim of radiative muon pairs (:math:`ee\\to\\mu\\mu(\\gamma)`) "
"for photon systematics."
)
__contact__ = __liaison__
__category__ = "systematics, photon calibration"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdMu("all", path=path)
[docs]
def build_lists(self, path):
# the tight selection starts with all muons, but they must be cluster-matched and not be an electron
MuonTightSelection = ("abs(dz) < 2.0 and abs(dr) < 0.5 and nCDCHits > 0 and "
"clusterE > 0.0 and clusterE < 1.0")
ma.cutAndCopyList("mu+:skimtight", "mu+:all", MuonTightSelection, path=path)
# for the loose selection starts with all muons, but we accept tracks that
# are not matched to a cluster, but if they are, they must not be an
# electron
MuonLooseSelection = "abs(dz) < 2.0 and abs(dr) < 0.5 and nCDCHits > 0 and clusterE < 1.0"
ma.cutAndCopyList("mu+:skimloose", "mu+:all", MuonLooseSelection, path=path)
# create a list of possible selections
radmumulist = []
# selection ID0:
# the radiative muon pair must be selected without looking at the photon.
# exclude events with more than two good tracks
RadMuMuSelection = "pRecoil > 0.075 and pRecoilTheta > 0.296706 and pRecoilTheta < 2.61799"
RadMuMuPairChannel = "mu+:skimtight mu-:skimloose"
chID = 0
ma.reconstructDecay("vpho:radmumu" + str(chID) + " -> " + RadMuMuPairChannel,
RadMuMuSelection, chID, path=path)
eventCuts = "nCleanedTracks(abs(dz) < 2.0 and abs(dr) < 0.5) == 2"
ma.applyCuts("vpho:radmumu" + str(chID), eventCuts, path=path)
radmumulist.append("vpho:radmumu" + str(chID))
# selection Id1:
# todo: include pair conversions?
return radmumulist
[docs]
@fancy_skim_header
class SystematicsEELL(BaseSkim):
__authors__ = ["Ilya Komarov"]
__description__ = "Systematics skim of :math:`ee\\to ee\\ell\\ell`"
__contact__ = __liaison__
__category__ = "systematics, lepton ID"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdE("all", path=path)
[docs]
def build_lists(self, path):
# At skim level we avoid any PID-like requirements and just select events
# with two good tracks coming from the interavtion region.
eLooseSelection = "abs(dz) < 2.0 and abs(dr) < 0.5 and p > 0.3"
ma.cutAndCopyList("e+:skimloose", "e+:all", eLooseSelection, path=path)
# create a list of possible selections
eelllist = []
# Lepon pair tracks are back-to-back-like
EELLSelection = "useCMSFrame(pt)<0.3"
eventCuts = "nCleanedTracks(abs(dz) < 2.0 and abs(dr) < 0.5) < 4"
ma.reconstructDecay("gamma:eell -> e+:skimloose e-:skimloose",
EELLSelection + " and " + eventCuts, path=path)
eelllist.append("gamma:eell")
return eelllist
[docs]
@fancy_skim_header
class SystematicsRadEE(BaseSkim):
"""
Constructed skim list contains radiative electron pairs for photon systematics. In
particular this is for the endcaps where we have no track triggers, we require one
cluster-matched electron (the other is not required to match a cluster). No
selection on the photon as the sample must be unbiased.
As this retains a lot of bhabha events (by construction) we allow for prescaling
(and prefer prescaled rather than a biased sampe by requiring any selection on the
photon or too much of a cut on the recoil momentum).
Prescales are given in standard trigger terms (reciprocal), so prescale of 100 is 1%
of events kept, *etc*.
"""
__authors__ = ["Sam Cunliffe"]
__description__ = "Radiative electron pairs for photon systematics"
__contact__ = __liaison__
__category__ = "systematics, photon calibration"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdE("all", path=path)
def __init__(self, prescale_all=1, prescale_fwd_electron=1, **kwargs):
"""
Parameters:
prescale_all (int): the global prescale for this skim
prescale_fwd_electron (int): the prescale electrons (e-) in
the forward endcap
**kwargs: Passed to constructor of `BaseSkim`.
"""
# Redefine __init__ to allow for additional optional arguments
super().__init__(**kwargs)
self.prescale_all = prescale_all
self.prescale_fwd_electron = prescale_fwd_electron
[docs]
def build_lists(self, path):
# convert prescales from trigger convention
prescale_all = str(float(1.0 / self.prescale_all))
prescale_fwd_electron = str(float(1.0 / self.prescale_fwd_electron))
# require a pair of good electrons one of which must be cluster-matched
# with 3 GeV of energy
goodtrack = "abs(dz) < 2.0 and abs(dr) < 0.5 and nCDCHits > 0"
goodtrackwithcluster = f"{goodtrack} and clusterE > 3.0"
ma.cutAndCopyList("e+:skimtight", "e+:all", goodtrackwithcluster, path=path)
ma.cutAndCopyList("e+:skimloose", "e+:all", goodtrack, path=path)
# a minimum momentum of 75 MeV/c recoiling against the pair,
# and require that the recoil is within the CDC acceptance
recoil = "pRecoil > 0.075 and 0.296706 < pRecoilTheta < 2.61799" # GeV/c, rad
ma.reconstructDecay("vpho:radee -> e+:skimtight e-:skimloose", recoil, path=path)
# apply event cuts (exactly two clean tracks in the event, and prescale
# the whole event regardless of where the electron went)
event_cuts = "[nCleanedTracks(abs(dz) < 2.0 and abs(dr) < 0.5) == 2]" # cm, cm
event_cuts += f" and [eventRandom <= {prescale_all}]"
# now prescale the *electron* (e-) in the forward endcap (for bhabhas)
# note this is all done with cut strings to circumnavigate BII-3607
fwd_encap_border = "0.5480334" # rad (31.4 deg)
electron_is_first = "daughter(0, charge) < 0"
first_in_fwd_endcap = f"daughter(0, theta) < {fwd_encap_border}"
first_not_in_fwd_endcap = f"daughter(0, theta) > {fwd_encap_border}"
electron_is_second = "daughter(1, charge) < 0"
second_in_fwd_endcap = f"daughter(1, theta) < {fwd_encap_border}"
second_not_in_fwd_endcap = f"daughter(1, theta) > {fwd_encap_border}"
passes_prescale = f"eventRandom <= {prescale_fwd_electron}"
#
# four possible scenarios:
# 1) electron first in the decaystring and in fwd endcap: prescale these
prescale_logic = f"[{electron_is_first} and {first_in_fwd_endcap} and {passes_prescale}]"
# 2) electron second in string and in fwd endcap: prescale these
prescale_logic += f" or [{electron_is_second} and {second_in_fwd_endcap} and {passes_prescale}]"
# 3) electron first in string and not in fwd endcap (no prescale)
prescale_logic += f" or [{electron_is_first} and {first_not_in_fwd_endcap}]"
# 4) electron second in string and not in fwd endcap (no prescale)
prescale_logic += f" or [{electron_is_second} and {second_not_in_fwd_endcap}]"
# final candidate building with cuts and prescales
prescale_logic = f"[{prescale_logic}]"
ma.applyCuts("vpho:radee", event_cuts + " and " + prescale_logic, path=path)
return ["vpho:radee"]
[docs]
@fancy_skim_header
class SystematicsLambda(BaseSkim):
__authors__ = ["Sam Cunliffe", "Torben Ferber", "Ilya Komarov", "Yuji Kato", "Jake Bennett"]
__description__ = ""
__contact__ = __liaison__
__category__ = "systematics"
ApplyHLTHadronCut = True
[docs]
def load_standard_lists(self, path):
stdLambdas(path=path)
[docs]
def build_lists(self, path):
vm.addAlias("fsig", "formula(flightDistance/flightDistanceErr)")
vm.addAlias("pMom", "daughter(0,p)")
vm.addAlias("piMom", "daughter(1,p)")
vm.addAlias("daughtersPAsym", "formula((pMom-piMom)/(pMom+piMom))")
LambdaList = []
ma.cutAndCopyList("Lambda0:syst0", "Lambda0:merged", "fsig>10 and daughtersPAsym>0.41", path=path)
LambdaList.append("Lambda0:syst0")
return LambdaList
[docs]
@fancy_skim_header
class SystematicsPhiGamma(BaseSkim):
"""
Uses the ``gamma:loose`` list and a cut on the number of tracks.
Cuts applied:
* :math:`E_{\\gamma}> 3\\,\\text{GeV}` AND
* :math:`E_{\\gamma}< 8\\,\\text{GeV}`
* :math:`n_{\\text{tracks}} \\geq 2` AND :math:`n_{\\text{tracks}} \\leq 4`
* at least 1 candidate in the K_S0:merged or in the phi->K+:all K-:all lists
"""
__authors__ = ["Giuseppe Finocchiaro", "Benjamin Oberhof"]
__description__ = (
"Skim for ISR - phi gamma analyses, "
":math:`e^+ e^- \\to \\phi \\gamma ` and "
":math:`\\phi` decays into two charged tracks "
"(:math:`K^+K^-` or :math:`K_S K_L` with :math:`K_S\\to \\pi^+\\pi^-`)"
)
__contact__ = "Giuseppe Finocchiaro <giuseppe.finocchiaro@lnf.infn.it>"
__category__ = "systematics"
ApplyHLTHadronCut = False
TestSampleProcess = "ccbar"
validation_sample = _VALIDATION_SAMPLE
[docs]
def load_standard_lists(self, path):
stdPhotons("loose", path=path)
stdK("all", path=path)
stdKshorts(path=path)
[docs]
def build_lists(self, path):
EventCuts = [
"[nTracks>=2] and [nTracks<=6]",
"[nParticlesInList(gamma:PhiSystematics) > 0]",
"[[nParticlesInList(phi:charged) > 0] or [nParticlesInList(K_S0:PhiSystematics) > 0]]"
]
ma.cutAndCopyList("gamma:PhiSystematics", "gamma:loose", "3 < E < 8", path=path)
ma.reconstructDecay('phi:charged -> K+:all K-:all', '0.9 < M < 1.2', path=path)
ma.copyList('K_S0:PhiSystematics', 'K_S0:merged', path=path)
path = self.skim_event_cuts(" and ".join(EventCuts), path=path)
return ["gamma:PhiSystematics"]
[docs]
def validation_histograms(self, path):
# NOTE: the validation package is not part of the light releases, so this import
# must be made here rather than at the top of the file.
from validation_tools.metadata import create_validation_histograms
vm.addAlias("gamma_E_CMS", "useCMSFrame(E)")
vm.addAlias("gamma_E", "E")
vm.addAlias("K_S0_mass", "M")
vm.addAlias("phi_mass", "M")
histoRootFile = f'{self}_Validation.root'
variableshisto = [('gamma_E', 120, 2.5, 8.5, 'gamma_E', self.__contact__, 'Photon energy', ''),
('gamma_E_CMS', 100, 2.0, 7.0, 'gamma_E_CMS', self.__contact__, 'Photon energy in CMS', ''),
('nTracks', 15, 0, 15, 'nTracks', self.__contact__, 'Number of tracks', ''),
]
variableshistoKS = [('K_S0_mass', 200, 0.4, 0.6, 'K_S0_mass', self.__contact__, 'Invariant KS0 mass', ''),
]
variableshistoPhi = [('phi_mass', 200, 0.8, 1.2, 'phi_mass', self.__contact__, 'Invariant phi mass', ''),
]
create_validation_histograms(path, histoRootFile, 'gamma:PhiSystematics', variableshisto)
create_validation_histograms(path, histoRootFile, 'K_S0:merged', variableshistoKS)
create_validation_histograms(path, histoRootFile, 'phi:charged', variableshistoPhi)
[docs]
@fancy_skim_header
class Random(BaseSkim):
__authors__ = "Phil Grace"
__contact__ = "Phil Grace <philip.grace@adelaide.edu.au>"
__description__ = "Random skim to select a fixed fraction of events."
__category__ = "systematics, random"
ApplyHLTHadronCut = False
def __init__(self, KeepPercentage=10, seed=None, **kwargs):
"""
Parameters:
KeepPercentage (float): Percentage of events to be kept.
seed (int): Set random seed to given number. If this argument is not given,
this skim will not alter the random seed.
**kwargs: Passed to constructor of `BaseSkim`.
"""
super().__init__(**kwargs)
self.KeepPercentage = KeepPercentage
self.seed = seed
[docs]
def additional_setup(self, path):
if self.seed is not None:
b2.set_random_seed(int(self.seed))
[docs]
def load_standard_lists(self, path):
stdPi("all", path=path)
stdPhotons("all", path=path)
[docs]
def build_lists(self, path):
# Select one photon/track per event with no other cuts, so that all events are
# captured in the skim list if KeepPercentage=100.
label = "RandomSkim"
ma.copyList(f"pi+:{label}", "pi+:all", path=path)
ma.copyList(f"gamma:{label}", "gamma:all", path=path)
ma.applyRandomCandidateSelection(f"pi+:{label}", path=path)
ma.applyRandomCandidateSelection(f"gamma:{label}", path=path)
# Select fraction of events
path = self.skim_event_cuts(
f"eventRandom <= {self.KeepPercentage/100}", path=path
)
return [f"pi+:{label}", f"gamma:{label}"]
[docs]
@fancy_skim_header
class SystematicsFourLeptonFromHLTFlag(BaseSkim):
__authors__ = "Marcel Hohmann"
__contact__ = __liaison_leptonID__
__description__ = "Skim to select all events that pass the HLT Four Lepton skim for lepton ID studies"
__category__ = "systematics, leptonID"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdPi("all", path=path)
[docs]
def build_lists(self, path):
label = "FourLeptonHLT"
ma.copyList(f"pi+:{label}", "pi+:all", path=path)
ma.rankByLowest(f"pi+:{label}", "random", 1, "systematicsFourLeptonHLT_randomRank", path=path)
path = self.skim_event_cuts(
"SoftwareTriggerResult(software_trigger_cut&skim&accept_fourlep) == 1", path=path
)
return [f"pi+:{label}"]
[docs]
@fancy_skim_header
class SystematicsRadMuMuFromHLTFlag(BaseSkim):
__authors__ = "Marcel Hohmann"
__contact__ = __liaison_leptonID__
__description__ = "Skim to select all events that pass the HLT RadMuMu skim for lepton ID studies"
__category__ = "systematics, leptonID"
ApplyHLTHadronCut = False
[docs]
def load_standard_lists(self, path):
stdPi("all", path=path)
[docs]
def build_lists(self, path):
label = "RadMuMuLeptonID"
ma.copyList(f"pi+:{label}", "pi+:all", path=path)
ma.rankByLowest(f"pi+:{label}", "random", 1, "systematicsRadMuMuLeptonID_randomRank", path=path)
path = self.skim_event_cuts(
"SoftwareTriggerResult(software_trigger_cut&skim&accept_radmumu) == 1", path=path
)
return [f"pi+:{label}"]
[docs]
@fancy_skim_header
class SystematicsJpsi(BaseSkim):
"""
J/psi skim for lepton ID systematics studies. Lists in this skim are those defined in `JpsimumuTagProbe`, `JpsieeTagProbe`.
"""
__authors__ = ["Sam Cunliffe", "Torben Ferber", "Ilya Komarov", "Yuji Kato", "Racha Cheaib", "Marcel Hohmann"]
__description__ = ""
__contact__ = __liaison_leptonID__
__category__ = "systematics, leptonID"
[docs]
def load_standard_lists(self, path):
stdMu("all", path=path)
stdE("all", path=path)
stdPhotons("all", path=path)
TestSampleProcess = "ccbar"
ApplyHLTHadronCut = True
[docs]
def build_lists(self, path):
return [
self.JpsimumuTagProbe(path),
self.JpsieeTagProbe(path),
]
[docs]
def JpsimumuTagProbe(self, path):
"""Build JpsimumuTagProbe lists for systematics skims."""
Cuts = "2.7 < M < 3.4"
ma.reconstructDecay(
"J/psi:systematics_mumu -> mu+:all mu-:all",
f'{Cuts} and [daughter(0,muonID)>0.1 or daughter(1,muonID)>0.1]',
path=path)
return "J/psi:systematics_mumu"
[docs]
def JpsieeTagProbe(self, path):
"""Build JpsieeTagProbe lists for systematics skims."""
Cuts = "2.7 < M < 3.4"
ma.cutAndCopyList('gamma:brems', 'gamma:all', 'E<1', path=path)
ma.correctBrems('e+:brems_corrected', 'e+:all', 'gamma:brems', path=path)
ma.reconstructDecay(
"J/psi:systematics_ee -> e+:brems_corrected e-:brems_corrected",
f'{Cuts} and [daughter(0,electronID_noTOP)>0.1 or daughter(1,electronID_noTOP)>0.1]',
path=path)
return "J/psi:systematics_ee"
[docs]
@fancy_skim_header
class SystematicsKshort(BaseSkim):
"""
K-short skim for hadron and lepton ID systematics studies.
As K-short candidates are abundant this skim has a high retention.
To meet the retention criteria a prescale is added. The prescale is given in standard trigger terms (reciprocal).
A prescale of 50 will keep 2% of events, etc.
"""
__authors__ = ["Marcel Hohmann"]
__description__ = "Skim for K-short events for performance studies"
__contact__ = __liaison_leptonID__
__category__ = "performance, leptonID"
ApplyHLTHadronCut = True
def __init__(self, prescale=4, **kwargs):
"""
Parameters:
prescale (int): the global prescale for this skim.
**kwargs: Passed to constructor of `BaseSkim`.
"""
self.prescale = prescale
super().__init__(**kwargs)
[docs]
def load_standard_lists(self, path):
stdPi("all", path=path)
[docs]
def build_lists(self, path):
ma.reconstructDecay(
'K_S0:reco -> pi+:all pi-:all',
'[0.30 < M < 0.70]',
path=path)
vertex.treeFit('K_S0:reco', 0.0, path=path)
ma.applyCuts('K_S0:reco', '0.4 < M < 0.6', path=path)
ma.fillParticleList('K_S0:v0 -> pi+ pi-',
'[0.30 < M < 0.70]',
True,
path=path)
vertex.treeFit('K_S0:v0', 0.0, path=path)
ma.applyCuts('K_S0:v0', '0.4 < M < 0.6', path=path)
ma.mergeListsWithBestDuplicate('K_S0:merged', ['K_S0:v0', 'K_S0:reco'],
variable='particleSource', preferLowest=True, path=path)
KS_cut = '[[cosAngleBetweenMomentumAndVertexVector>0.998] or '\
' [formula(flightDistance/flightDistanceErr)>11] or '\
' [flightTime>0.007]]' # and '\
# '[useAlternativeDaughterHypothesis(M, 0:p+) > 1.13068 and useAlternativeDaughterHypothesis(M, 0:pi-, 1:p+) > 1.13068]'
ma.cutAndCopyList("K_S0:skim", "K_S0:merged", KS_cut, path=path)
path = self.skim_event_cuts(f'eventRandom < {(1/self.prescale):.6f}', path=path)
return ['K_S0:skim']
[docs]
@fancy_skim_header
class SystematicsBhabha(BaseSkim):
"""
Skim for selecting Bhabha events for leptonID studies.
In case the retention exceeds 10% a prescale can be added.
The prescale is given in standard trigger terms (reciprocal).
"""
__authors__ = ["Justin Skorupa"]
__description__ = "Skim for Bhabha events for lepton ID study"
__contact__ = __liaison_leptonID__
__category__ = "performance, leptonID"
ApplyHLTHadronCut = False
def __init__(self, prescale=1, **kwargs):
"""
Parameters:
prescale (int): the global prescale for this skim.
**kwargs: Passed to constructor of `BaseSkim`.
"""
self.prescale = prescale
super().__init__(**kwargs)
[docs]
def load_standard_lists(self, path):
stdE("all", path=path)
[docs]
def build_lists(self, path):
goodtrack = "abs(dz) < 5 and abs(dr) < 2"
goodtrackwithPID = f"{goodtrack} and electronID_noTOP > 0.95 and clusterTheta > 0.59"\
" and clusterTheta < 2.15 and useCMSFrame(clusterE) > 2"
ma.cutAndCopyList("e+:tight", "e+:all", goodtrackwithPID, path=path)
ma.cutAndCopyList("e+:loose", "e+:all", goodtrack, path=path)
ma.reconstructDecay(
"vpho:bhabha -> e+:tight e-:loose", "", path=path)
event_cuts = "[nCleanedTracks(abs(dz) < 5 and abs(dr) < 2) == 2]"\
f" and eventRandom < {(1/self.prescale):.6f}"
ma.applyCuts("vpho:bhabha", event_cuts, path=path)
return ["vpho:bhabha"]
[docs]
@fancy_skim_header
class SystematicsCombinedHadronic(CombinedSkim):
"""
Combined systematics skim for the four hadronic channels:
SystematicsKshort,
SystematicsJpsi,
SystematicsDstar,
SystemmaticsLambda.
This is required for technical (data production) reasons, as it keeps the number of files low.
See the definitions of the individual skims for the details.
"""
__authors__ = ["Marcel Hohmann"]
__description__ = "Combined Skim of the systematic hadronic skims: Kshort, Jpsi, Dstar, Lambda."
__contact__ = __liaison_leptonID__
__category__ = "performance, leptonID"
__name__ = "SystematicsCombinedHadronic"
produces_mdst_by_default = True
def __init__(self, prescale_kshort=4, mdstOutput=True, **kwargs):
""" Initialiser.
Args:
prescale_kshort (Optional[int]): offline prescale factor for KS skim.
**kwargs: key-worded arguments. See CombinedSkim.__init__()
"""
kwargs.update(mdstOutput=mdstOutput, CombinedSkimName=self.__name__)
kwargs.setdefault('udstOutput', False)
skims_list = [SystematicsKshort(prescale=prescale_kshort), SystematicsDstar(), SystematicsLambda(), SystematicsJpsi()]
super().__init__(*skims_list, **kwargs)
[docs]
@fancy_skim_header
class SystematicsCombinedLowMulti(CombinedSkim):
"""
Combined systematics skim for the four low multi channels:
SystematicsFourLeptonFromHLTFlag,
SystematicsRadmumuFromHLTFlag,
SystematicsBhabha,
TauThrust.
This is required for technical (data production) reasons, as it keeps the number of files low.
See the definitions of the individual skims for the details.
"""
__authors__ = ["Marcel Hohmann"]
__description__ = "Combined Skim of the systematic low multi skims: FourLepton, Radmumu, Bhabha, TauThrust."
__contact__ = __liaison_leptonID__
__category__ = "performance, leptonID"
__name__ = "SystematicsCombinedLowMulti"
produces_mdst_by_default = True
def __init__(self, mdstOutput=True, **kwargs):
""" Initialiser.
Args:
**kwargs: key-worded arguments. See CombinedSkim.__init__()
"""
kwargs.update(mdstOutput=mdstOutput, CombinedSkimName=self.__name__)
kwargs.setdefault('udstOutput', False)
from skim.WGs.taupair import TauThrust
skims_list = [SystematicsFourLeptonFromHLTFlag(), SystematicsRadMuMuFromHLTFlag(), SystematicsBhabha(), TauThrust()]
super().__init__(*skims_list, **kwargs)
