#!/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. #
##########################################################################
"""
(Semi-)Leptonic Working Group Skims for missing energy modes that use the `FullEventInterpretation` (FEI) algorithm.
"""
from functools import lru_cache, wraps
import basf2 as b2
import fei
import modularAnalysis as ma
from skim import BaseSkim, fancy_skim_header
from skim.utils.misc import _sphinxify_decay
from variables import variables as vm
__liaison__ = "Cameron Harris <cameron.harris@adelaide.edu.au>, Tommy Martinov <tommy.martinov@desy.de>"
_VALIDATION_SAMPLE = "mdst16.root"
def _merge_boolean_dicts(*dicts):
"""Merge dicts of boolean, with `True` values taking precedence if values
differ.
This is a utility function for combining FEI configs. It acts in the following
way:
>>> d1 = {"neutralB": True, "chargedB": False, "hadronic": True}
>>> d2 = {"chargedB": True, "semileptonic": True}
>>> _merge_FEI_configs(d1, d2)
{"chargedB": True, "hadronic": True, "neutralB": True, "semileptonic": True}
Parameters:
dicts (dict(str -> bool)): Any number of dicts of keyword-boolean pairs.
Returns:
merged (dict(str -> bool)): A single dict, containing all the keys of the
input dicts.
"""
keys = {k for d in dicts for k in d}
occurances = {k: [d for d in dicts if k in d] for k in keys}
merged = {k: any(d[k] for d in occurances[k]) for k in keys}
# Sort the merged dict before returning
merged = dict(sorted(merged.items()))
return merged
def _get_fei_channel_names(particleName, **kwargs):
"""Create a list containing the decay strings of all decay channels available to a
particle. Any keyword arguments are passed to `fei.get_default_channels`.
This is a utility function for autogenerating FEI skim documentation.
Args:
particleName (str): the PDG name of a particle, e.g. ``'K+'``, ``'pi-'``, ``'D*0'``.
"""
particleList = fei.get_default_channels(**kwargs)
particleDict = {particle.name: particle for particle in particleList}
try:
particle = particleDict[particleName]
except KeyError:
print(f"Error! Couldn't find particle with name {particleName}")
return []
channels = [channel.decayString for channel in particle.channels]
return channels
def _hash_dict(func):
"""Wrapper for `functools.lru_cache` to deal with dictionaries. Dictionaries are
mutable, so cannot be cached. This wrapper turns all dict arguments into a hashable
dict type, so we can use caching.
"""
class HashableDict(dict):
def __hash__(self):
return hash(frozenset(self.items()))
@wraps(func)
def wrapped(*args, **kwargs):
args = tuple([HashableDict(arg) if isinstance(arg, dict) else arg for arg in args])
kwargs = {k: HashableDict(v) if isinstance(v, dict) else v for k, v in kwargs.items()}
return func(*args, **kwargs)
return wrapped
[docs]
class BaseFEISkim(BaseSkim):
"""Base class for FEI skims. Applies event-level pre-cuts and applies the FEI."""
__authors__ = ["Racha Cheaib", "Hannah Wakeling", "Phil Grace"]
__contact__ = __liaison__
__category__ = "physics, Full Event Interpretation"
FEIPrefix = "FEIv1_2025_MC16ri_aldebaran_200"
"""Prefix label for the FEI training used in the FEI skims."""
FEIChannelArgs = {}
"""Dict of ``str -> bool`` pairs to be passed to `fei.get_default_channels`. When
inheriting from `BaseFEISkim`, override this value to apply the FEI for only *e.g.*
SL charged :math:`B`'s."""
MergeDataStructures = {"FEIChannelArgs": _merge_boolean_dicts}
NoisyModules = ["ParticleCombiner"]
ApplyHLTHadronCut = True
produce_on_tau_samples = True # Note retention is very close to zero on taupair
[docs]
@staticmethod
@lru_cache()
def fei_precuts(path):
"""
Skim pre-cuts are applied before running the FEI, to reduce computation time.
This setup function is run by all FEI skims, so they all have the save
event-level pre-cuts:
* :math:`n_{\\text{cleaned tracks}} \\geq 3`
* :math:`n_{\\text{cleaned ECL clusters}} \\geq 3`
* :math:`\\text{Visible energy of event (CMS frame)}>4~{\\rm GeV}`
We define "cleaned" tracks and clusters as:
* Cleaned tracks (``pi+:FEI_cleaned``): :math:`d_0 < 0.5~{\\rm cm}`,
:math:`|z_0| < 2~{\\rm cm}`, and :math:`p_T > 0.1~{\\rm GeV}`
* Cleaned ECL clusters (``gamma:FEI_cleaned``):
:math:`\\theta` in CDC acceptance, and
:math:`E>0.1~{\\rm GeV}`
"""
# Pre-selection cuts
CleanedTrackCuts = "abs(dz) < 2.0 and abs(dr) < 0.5 and pt > 0.1"
CleanedClusterCuts = "E > 0.1 and thetaInCDCAcceptance"
ma.fillParticleList(decayString="pi+:FEI_cleaned",
cut=CleanedTrackCuts, path=path)
ma.fillParticleList(decayString="gamma:FEI_cleaned",
cut=CleanedClusterCuts, path=path)
ma.buildEventKinematics(inputListNames=["pi+:FEI_cleaned",
"gamma:FEI_cleaned"],
path=path)
EventCuts = " and ".join(
[
f"nCleanedTracks({CleanedTrackCuts})>=3",
f"nCleanedECLClusters({CleanedClusterCuts})>=3",
"visibleEnergyOfEventCMS>4",
]
)
# NOTE: The FEI skims are somewhat complicated, and require some manual handling
# of conditional paths to avoid adding the FEI to the path twice. In general, DO
# NOT do this kind of path handling in your own skim. Instead, use:
# >>> path = self.skim_event_cuts(EventLevelCuts, path=path)
ConditionalPath = b2.Path()
eselect = path.add_module("VariableToReturnValue", variable=f"passesEventCut({EventCuts})")
eselect.if_value('=1', ConditionalPath, b2.AfterConditionPath.CONTINUE)
return ConditionalPath
# This is a cached static method so that we can avoid adding FEI path twice.
# In combined skims, FEIChannelArgs must be combined across skims first, so that all
# the required particles are included in the FEI.
[docs]
@staticmethod
@_hash_dict
@lru_cache()
def run_fei_for_skims(FEIChannelArgs, FEIPrefix, analysisGlobaltag, *, path):
"""Reconstruct hadronic and semileptonic :math:`B^0` and :math:`B^+` tags using
the generically trained FEI.
Parameters:
FEIChannelArgs (dict(str, bool)): A dict of keyword-boolean pairs to be
passed to `fei.get_default_channels`.
FEIPrefix (str): Prefix label for the FEI training used in the FEI skims.
path (`basf2.Path`): The skim path to be processed.
"""
# Run FEI
if analysisGlobaltag is None:
b2.B2FATAL("The analysis globaltag is not set in the FEI skim.")
b2.conditions.prepend_globaltag(analysisGlobaltag)
particles = fei.get_default_channels(**FEIChannelArgs)
configuration = fei.config.FeiConfiguration(
prefix=FEIPrefix,
training=False,
monitor=False)
feistate = fei.get_path(particles, configuration)
path.add_path(feistate.path)
[docs]
@staticmethod
@_hash_dict
@lru_cache()
def setup_fei_aliases(FEIChannelArgs):
# Aliases for pre-FEI event-level cuts
vm.addAlias("E_ECL_pi_FEI",
"totalECLEnergyOfParticlesInList(pi+:FEI_cleaned)")
vm.addAlias("E_ECL_gamma_FEI",
"totalECLEnergyOfParticlesInList(gamma:FEI_cleaned)")
vm.addAlias("E_ECL_FEI", "formula(E_ECL_pi_FEI+E_ECL_gamma_FEI)")
# Aliases for variables available after running the FEI
vm.addAlias("sigProb", "extraInfo(SignalProbability)")
vm.addAlias("log10_sigProb", "log10(extraInfo(SignalProbability))")
vm.addAlias("dmID", "extraInfo(decayModeID)")
vm.addAlias("decayModeID", "extraInfo(decayModeID)")
if "semileptonic" in FEIChannelArgs and FEIChannelArgs["semileptonic"]:
# Aliases specific to SL FEI
vm.addAlias("cosThetaBY", "cosThetaBetweenParticleAndNominalB")
vm.addAlias("d1_p_CMSframe", "useCMSFrame(daughter(1,p))")
vm.addAlias("d2_p_CMSframe", "useCMSFrame(daughter(2,p))")
vm.addAlias(
"p_lepton_CMSframe",
"conditionalVariableSelector(dmID<4, d1_p_CMSframe, d2_p_CMSframe)"
)
[docs]
def additional_setup(self, path):
"""Apply pre-FEI event-level cuts and apply the FEI. This setup function is run
by all FEI skims, so they all have the same event-level pre-cuts.
This function passes `FEIChannelArgs` to the cached function `run_fei_for_skims`
to avoid applying the FEI twice.
See also:
`fei_precuts` for event-level cut definitions.
"""
self.setup_fei_aliases(self.FEIChannelArgs)
path = self.fei_precuts(path)
# The FEI skims require some manual handling of paths that is not necessary in
# any other skim.
self._ConditionalPath = path
self.run_fei_for_skims(self.FEIChannelArgs, self.FEIPrefix, self.analysisGlobaltag, path=path)
def _FEI_skim_header(ParticleNames):
"""Decorator factory for applying the `fancy_skim_header` header and replacing
<CHANNELS> in the class docstring with a list of FEI channels.
The list is numbered with all of the corresponding decay mode IDs, and the decay
modes are formatted in beautiful LaTeX.
.. code-block:: python
@FEI_skim_header("B0")
class feiSLB0(BaseFEISkim):
# docstring here including the string '<CHANNELS>' somewhere
Parameters:
ParticleNames (str, list(str)): One of either ``B0`` or ``B+``, or a list of both.
"""
def decorator(SkimClass):
if isinstance(ParticleNames, str):
particles = [ParticleNames]
else:
particles = ParticleNames
ChannelsString = "List of reconstructed channels and corresponding decay mode IDs:"
for particle in particles:
channels = _get_fei_channel_names(particle, **SkimClass.FEIChannelArgs)
FormattedChannels = [_sphinxify_decay(channel) for channel in channels]
ChannelList = "\n".join(
[f" {dmID}. {channel}"
for (dmID, channel) in enumerate(FormattedChannels)]
)
if len(particles) == 1:
ChannelsString += "\n\n" + ChannelList
else:
ChannelsString += f"\n\n ``{particle}`` channels:\n\n" + ChannelList
if SkimClass.__doc__ is None:
return SkimClass
else:
SkimClass.__doc__ = SkimClass.__doc__.replace("<CHANNELS>", ChannelsString)
return fancy_skim_header(SkimClass)
return decorator
[docs]
@_FEI_skim_header("B0")
class feiHadronicB0(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`M_{\\text{bc}} > 5.2~{\\rm GeV}`
* :math:`|\\Delta E| < 0.3~{\\rm GeV}`
* :math:`\\text{signal probability} > 0.001` (omitted for decay mode 23)
All available FEI :math:`B^0` hadronic tags are reconstructed. From `Thomas Keck's
thesis <https://docs.belle2.org/record/275/files/BELLE2-MTHESIS-2015-001.pdf>`_,
"the channel :math:`B^0 \\to \\overline{D}^0 \\pi^0` was used by the FR, but is not
yet used in the FEI due to unexpected technical restrictions in the KFitter
algorithm".
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = "FEI-tagged neutral :math:`B`'s decaying hadronically."
validation_sample = _VALIDATION_SAMPLE
FEIChannelArgs = {
"neutralB": True,
"chargedB": False,
"hadronic": True,
"semileptonic": False,
"KLong": False,
"baryonic": True
}
[docs]
def build_lists(self, path):
ma.applyCuts("B0:generic", "Mbc > 5.2 and abs(deltaE) < 0.3 and [sigProb > 0.001 or extraInfo(dmID)==23]", path=path)
return ["B0:generic"]
[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('sigProb', 'extraInfo(SignalProbability)')
vm.addAlias('log10_sigProb', 'log10(extraInfo(SignalProbability))')
vm.addAlias('d0_massDiff', 'daughter(0,massDifference(0))')
vm.addAlias('d0_M', 'daughter(0,M)')
vm.addAlias('decayModeID', 'extraInfo(decayModeID)')
vm.addAlias('nDaug', 'countDaughters(1>0)') # Dummy cut so all daughters are selected.
histogramFilename = f"{self}_Validation.root"
create_validation_histograms(
rootfile=histogramFilename,
particlelist='B0:generic',
variables_1d=[
('sigProb', 100, 0.0, 1.0, 'Signal probability', __liaison__,
'Signal probability of the reconstructed tag B candidates',
'Most around zero, with a tail at non-zero values.', 'Signal probability', 'Candidates', 'logy'),
('nDaug', 6, 0.0, 6, 'Number of daughters of tag B', __liaison__,
'Number of daughters of tag B', 'Some distribution of number of daughters', 'n_{daughters}', 'Candidates'),
('d0_massDiff', 100, 0.0, 0.5, 'Mass difference of D* and D', __liaison__,
'Mass difference of D^{*} and D', 'Peak at 0.14 GeV', 'm(D^{*})-m(D) [GeV]', 'Candidates', 'shifter'),
('d0_M', 100, 0.0, 3.0, 'Mass of zeroth daughter (D* or D)', __liaison__,
'Mass of zeroth daughter of tag B (either a $D^{*}$ or a D)', 'Peaks at 1.86 GeV and 2.00 GeV',
'm(D^{(*)}) [GeV]', 'Candidates', 'shifter'),
('deltaE', 40, -0.3, 0.3, '#Delta E', __liaison__,
'$\\Delta E$ of event', 'Peak around zero', '#Delta E [GeV]', 'Candidates', 'shifter'),
('Mbc', 40, 5.2, 5.3, 'Mbc', __liaison__,
'Beam-constrained mass of event', 'Peaking around B mass (5.28 GeV)', 'M_{bc} [GeV]', 'Candidates', 'shifter')],
variables_2d=[('deltaE', 100, -0.3, 0.3, 'Mbc', 100, 5.2, 5.3, 'Mbc vs deltaE', __liaison__,
'Plot of the $\\Delta E$ of the event against the beam constrained mass',
'Peak of $\\Delta E$ around zero, and $M_{bc}$ around B mass (5.28 GeV)',
'#Delta E [GeV]', 'M_{bc} [GeV]', 'colz'),
('decayModeID', 26, 0, 26, 'log10_sigProb', 100, -3.0, 0.0,
'Signal probability for each decay mode ID', __liaison__,
'Signal probability for each decay mode ID',
'Some distribution of candidates in the first few decay mode IDs',
'Decay mode ID', '#log_10(signal probability)', 'colz')],
path=path)
[docs]
@_FEI_skim_header("B+")
class feiHadronicBplus(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`M_{\\text{bc}} > 5.2~{\\rm GeV}`
* :math:`|\\Delta E| < 0.3~{\\rm GeV}`
* :math:`\\text{signal probability} > 0.001` (omitted for decay mode 25)
All available FEI :math:`B^+` hadronic tags are reconstructed.
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = "FEI-tagged charged :math:`B`'s decaying hadronically."
validation_sample = _VALIDATION_SAMPLE
FEIChannelArgs = {
"neutralB": False,
"chargedB": True,
"hadronic": True,
"semileptonic": False,
"KLong": False,
"baryonic": True
}
[docs]
def build_lists(self, path):
ma.applyCuts("B+:generic", "Mbc > 5.2 and abs(deltaE) < 0.3 and [sigProb > 0.001 or extraInfo(dmID)==25]", path=path)
return ["B+:generic"]
[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('sigProb', 'extraInfo(SignalProbability)')
vm.addAlias('log10_sigProb', 'log10(extraInfo(SignalProbability))')
vm.addAlias('d0_massDiff', 'daughter(0,massDifference(0))')
vm.addAlias('d0_M', 'daughter(0,M)')
vm.addAlias('decayModeID', 'extraInfo(decayModeID)')
vm.addAlias('nDaug', 'countDaughters(1>0)') # Dummy cut so all daughters are selected.
histogramFilename = f"{self}_Validation.root"
create_validation_histograms(
rootfile=histogramFilename,
particlelist='B+:generic',
variables_1d=[
('sigProb', 100, 0.0, 1.0, 'Signal probability', __liaison__,
'Signal probability of the reconstructed tag B candidates', 'Most around zero, with a tail at non-zero values.',
'Signal probability', 'Candidates', 'logy'),
('nDaug', 6, 0.0, 6, 'Number of daughters of tag B', __liaison__,
'Number of daughters of tag B', 'Some distribution of number of daughters', 'n_{daughters}', 'Candidates'),
('d0_massDiff', 100, 0.0, 0.5, 'Mass difference of D* and D', __liaison__,
'Mass difference of D^{*} and D', 'Peak at 0.14 GeV', 'm(D^{*})-m(D) [GeV]', 'Candidates', 'shifter'),
('d0_M', 100, 0.0, 3.0, 'Mass of zeroth daughter (D* or D)', __liaison__,
'Mass of zeroth daughter of tag B (either a $D^{*}$ or a D)', 'Peaks at 1.86 GeV and 2.00 GeV',
'm(D^{(*)}) [GeV]', 'Candidates', 'shifter'),
('deltaE', 40, -0.3, 0.3, '#Delta E', __liaison__,
'$\\Delta E$ of event', 'Peak around zero', '#Delta E [GeV]', 'Candidates', 'shifter'),
('Mbc', 40, 5.2, 5.3, 'Mbc', __liaison__,
'Beam-constrained mass of event', 'Peak around B mass (5.28 GeV)', 'M_{bc} [GeV]', 'Candidates', 'shifter')],
variables_2d=[('deltaE', 100, -0.3, 0.3, 'Mbc', 100, 5.2, 5.3, 'Mbc vs deltaE', __liaison__,
'Plot of the $\\Delta E$ of the event against the beam constrained mass',
'Peak of $\\Delta E$ around zero, and $M_{bc}$ around B mass (5.28 GeV)',
'#Delta E [GeV]', 'M_{bc} [GeV]', 'colz'),
('decayModeID', 29, 0, 29, 'log10_sigProb', 100, -3.0, 0.0,
'Signal probability for each decay mode ID', __liaison__,
'Signal probability for each decay mode ID',
'Some distribution of candidates in the first few decay mode IDs',
'Decay mode ID', '#log_10(signal probability)', 'colz')],
path=path)
[docs]
@_FEI_skim_header("B0")
class feiSLB0(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`-4 < \\cos\\theta_{BY} < 3`
* :math:`\\log_{10}(\\text{signal probability}) > -2.4`
* :math:`p_{\\ell}^{*} > 1.0~{\\rm GeV}` in CMS frame
SL :math:`B^0` tags are reconstructed. Hadronic :math:`B` with SL :math:`D` are not
reconstructed, as these are rare and time-intensive.
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = "FEI-tagged neutral :math:`B`'s decaying semileptonically."
validation_sample = _VALIDATION_SAMPLE
FEIChannelArgs = {
"neutralB": True,
"chargedB": False,
"hadronic": False,
"semileptonic": True,
"KLong": False,
"baryonic": True,
"removeSLD": True
}
[docs]
def build_lists(self, path):
Bcuts = " and ".join(["dmID < 8", "log10(sigProb) > -2.4", "-4 < cosThetaBY < 3", "p_lepton_CMSframe > 1"])
ma.applyCuts("B0:semileptonic", Bcuts, path=path)
return ["B0:semileptonic"]
[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('sigProb', 'extraInfo(SignalProbability)')
vm.addAlias('log10_sigProb', 'log10(extraInfo(SignalProbability))')
vm.addAlias('d0_massDiff', 'daughter(0,massDifference(0))')
vm.addAlias('d0_M', 'daughter(0,M)')
vm.addAlias('decayModeID', 'extraInfo(decayModeID)')
vm.addAlias('nDaug', 'countDaughters(1>0)') # Dummy cut so all daughters are selected.
histogramFilename = f"{self}_Validation.root"
create_validation_histograms(
rootfile=histogramFilename,
particlelist='B0:semileptonic',
variables_1d=[
('sigProb', 100, 0.0, 1.0, 'Signal probability', __liaison__,
'Signal probability of the reconstructed tag B candidates', 'Most around zero, with a tail at non-zero values.',
'Signal probability', 'Candidates', 'logy'),
('nDaug', 6, 0.0, 6, 'Number of daughters of tag B', __liaison__,
'Number of daughters of tag B', 'Some distribution of number of daughters', 'n_{daughters}', 'Candidates'),
('cosThetaBetweenParticleAndNominalB', 100, -6.0, 4.0, '#cos#theta_{BY}', __liaison__,
'Cosine of angle between the reconstructed B and the nominal B', 'Distribution peaking between -1 and 1',
'#cos#theta_{BY}', 'Candidates'),
('d0_massDiff', 100, 0.0, 0.5, 'Mass difference of D* and D', __liaison__,
'Mass difference of $D^{*}$ and D', 'Peak at 0.14 GeV', 'm(D^{*})-m(D) [GeV]', 'Candidates', 'shifter'),
('d0_M', 100, 0.0, 3.0, 'Mass of zeroth daughter (D* or D)', __liaison__,
'Mass of zeroth daughter of tag B (either a $D^{*}$ or a D)', 'Peaks at 1.86 GeV and 2.00 GeV',
'm(D^{(*)}) [GeV]', 'Candidates', 'shifter')],
variables_2d=[('decayModeID', 8, 0, 8, 'log10_sigProb', 100, -3.0, 0.0,
'Signal probability for each decay mode ID', __liaison__,
'Signal probability for each decay mode ID',
'Some distribution of candidates in the first few decay mode IDs',
'Decay mode ID', '#log_10(signal probability)', 'colz')],
path=path)
[docs]
@_FEI_skim_header("B0")
class feiSLB0_RDstar(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`\\text{FoxWolframR2} < 0.4`
* :math:`-1.75 < \\cos\\theta_{BY} < 1.1`
* :math:`\\log_{10}(\\text{signal probability}) > -2.0`
* :math:`p_{\\ell}^{*} > 1.0~{\\rm GeV}` in CMS frame
* :math:`\\text{BCS:signal probability}`
SL :math:`B^0` tags are reconstructed. Hadronic :math:`B` with SL :math:`D` are not
reconstructed, as these are rare and time-intensive.
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = ("FEI-tagged neutral :math:`B`'s decaying semileptonically",
"Analysis cuts included, best sigProb candidate kept"
)
validation_sample = _VALIDATION_SAMPLE
FEIChannelArgs = {
"neutralB": True,
"chargedB": False,
"hadronic": False,
"semileptonic": True,
"KLong": False,
"baryonic": True,
"removeSLD": True
}
[docs]
def build_lists(self, path):
ma.buildEventShape(inputListNames=['pi+:FEI_cleaned', 'gamma:FEI_cleaned'],
foxWolfram=True,
harmonicMoments=False,
cleoCones=False,
thrust=False,
collisionAxis=False,
jets=False,
sphericity=False,
checkForDuplicates=True,
path=path)
# tightened cuts on sigprob and cosThetaBY as well as additional cut on Fox WR R2
vm.addAlias('foxWolframR2_maskedNaN', 'ifNANgiveX(foxWolframR2,1)')
TighterCuts = " and ".join(
[
"foxWolframR2_maskedNaN<0.4",
"dmID < 8",
"log10(sigProb) > -2.0",
"-1.75 < cosThetaBY < 1.1",
"p_lepton_CMSframe > 1.0"
]
)
ma.cutAndCopyList("B0:SLRDstar", "B0:semileptonic", TighterCuts, path=path)
# best candidate selection on signal probability
ma.rankByHighest("B0:SLRDstar", "sigProb", numBest=1,
allowMultiRank=True, outputVariable='sigProb_rank_tag',
path=path)
return ["B0:SLRDstar"]
[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('sigProb', 'extraInfo(SignalProbability)')
vm.addAlias('log10_sigProb', 'log10(extraInfo(SignalProbability))')
vm.addAlias('d0_massDiff', 'daughter(0,massDifference(0))')
vm.addAlias('d0_M', 'daughter(0,M)')
vm.addAlias('decayModeID', 'extraInfo(decayModeID)')
vm.addAlias('nDaug', 'countDaughters(1>0)') # Dummy cut so all daughters are selected.
histogramFilename = f"{self}_Validation.root"
create_validation_histograms(
rootfile=histogramFilename,
particlelist='B0:SLRDstar',
variables_1d=[
('sigProb', 100, 0.0, 1.0, 'Signal probability', __liaison__,
'Signal probability of the reconstructed tag B candidates', 'Most around zero, with a tail at non-zero values.',
'Signal probability', 'Candidates', 'logy'),
('nDaug', 6, 0.0, 6, 'Number of daughters of tag B', __liaison__,
'Number of daughters of tag B', 'Some distribution of number of daughters', 'n_{daughters}', 'Candidates'),
('cosThetaBetweenParticleAndNominalB', 100, -6.0, 4.0, '#cos#theta_{BY}', __liaison__,
'Cosine of angle between the reconstructed B and the nominal B', 'Distribution peaking between -1 and 1',
'#cos#theta_{BY}', 'Candidates'),
('d0_massDiff', 100, 0.0, 0.5, 'Mass difference of D* and D', __liaison__,
'Mass difference of $D^{*}$ and D', 'Peak at 0.14 GeV', 'm(D^{*})-m(D) [GeV]', 'Candidates', 'shifter'),
('d0_M', 100, 0.0, 3.0, 'Mass of zeroth daughter (D* or D)', __liaison__,
'Mass of zeroth daughter of tag B (either a $D^{*}$ or a D)', 'Peaks at 1.86 GeV and 2.00 GeV',
'm(D^{(*)}) [GeV]', 'Candidates', 'shifter')],
variables_2d=[('decayModeID', 8, 0, 8, 'log10_sigProb', 100, -3.0, 0.0,
'Signal probability for each decay mode ID', __liaison__,
'Signal probability for each decay mode ID',
'Some distribution of candidates in the first few decay mode IDs',
'Decay mode ID', '#log_10(signal probability)', 'colz')],
path=path)
[docs]
@_FEI_skim_header("B+")
class feiSLBplus(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`-4 < \\cos\\theta_{BY} < 3`
* :math:`\\log_{10}(\\text{signal probability}) > -2.4`
* :math:`p_{\\ell}^{*} > 1.0~{\\rm GeV}` in CMS frame
SL :math:`B^+` tags are reconstructed. Hadronic :math:`B^+` with SL :math:`D` are
not reconstructed, as these are rare and time-intensive.
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = "FEI-tagged charged :math:`B`'s decaying semileptonically."
validation_sample = _VALIDATION_SAMPLE
FEIChannelArgs = {
"neutralB": False,
"chargedB": True,
"hadronic": False,
"semileptonic": True,
"KLong": False,
"baryonic": True,
"removeSLD": True
}
[docs]
def build_lists(self, path):
Bcuts = " and ".join(["dmID < 8", "log10_sigProb > -2.4", "-4 < cosThetaBY < 3", "p_lepton_CMSframe > 1"])
ma.applyCuts("B+:semileptonic", Bcuts, path=path)
return ["B+:semileptonic"]
[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('sigProb', 'extraInfo(SignalProbability)')
vm.addAlias('log10_sigProb', 'log10(extraInfo(SignalProbability))')
vm.addAlias('d0_massDiff', 'daughter(0,massDifference(0))')
vm.addAlias('d0_M', 'daughter(0,M)')
vm.addAlias('decayModeID', 'extraInfo(decayModeID)')
vm.addAlias('nDaug', 'countDaughters(1>0)') # Dummy cut so all daughters are selected.
histogramFilename = f"{self}_Validation.root"
create_validation_histograms(
rootfile=histogramFilename,
particlelist='B+:semileptonic',
variables_1d=[
('sigProb', 100, 0.0, 1.0, 'Signal probability', __liaison__,
'Signal probability of the reconstructed tag B candidates',
'Most around zero, with a tail at non-zero values.', 'Signal probability', 'Candidates', 'logy'),
('nDaug', 6, 0.0, 6, 'Number of daughters of tag B', __liaison__,
'Number of daughters of tag B', 'Some distribution of number of daughters', 'n_{daughters}', 'Candidates'),
('cosThetaBetweenParticleAndNominalB', 100, -6.0, 4.0, '#cos#theta_{BY}', __liaison__,
'Cosine of angle between the reconstructed B and the nominal B', 'Distribution peaking between -1 and 1',
'#cos#theta_{BY}', 'Candidates'),
('d0_massDiff', 100, 0.0, 0.5, 'Mass difference of D* and D', __liaison__,
'Mass difference of $D^{*}$ and D', 'Peak at 0.14 GeV', 'm(D^{*})-m(D) [GeV]', 'Candidates', 'shifter'),
('d0_M', 100, 0.0, 3.0, 'Mass of zeroth daughter (D* or D)', __liaison__,
'Mass of zeroth daughter of tag B (either a $D^{*}$ or a D)', 'Peaks at 1.86 GeV and 2.00 GeV',
'm(D^{(*)}) [GeV]', 'Candidates', 'shifter')],
variables_2d=[('decayModeID', 8, 0, 8, 'log10_sigProb', 100, -3.0, 0.0,
'Signal probability for each decay mode ID', __liaison__,
'Signal probability for each decay mode ID',
'Some distribution of candidates in the first few decay mode IDs',
'Decay mode ID', '#log_10(signal probability)', 'colz')],
path=path)
[docs]
@_FEI_skim_header(["B0", "B+"])
class feiHadronic(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`M_{\\text{bc}} > 5.2~{\\rm GeV}`
* :math:`|\\Delta E| < 0.3~{\\rm GeV}`
* :math:`\\text{signal probability} > 0.001` (omitted for decay mode 25 for
:math:`B^+`, and decay mode 23 for :math:`B^0`)
All available FEI :math:`B^0` and :math:`B^+` hadronic tags are reconstructed. From
`Thomas Keck's thesis
<https://docs.belle2.org/record/275/files/BELLE2-MTHESIS-2015-001.pdf>`_, "the
channel :math:`B^0 \\to \\overline{D}^0 \\pi^0` was used by the FR, but is not yet
used in the FEI due to unexpected technical restrictions in the KFitter algorithm".
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = "FEI-tagged neutral and charged :math:`B`'s decaying hadronically."
FEIChannelArgs = {
"neutralB": True,
"chargedB": True,
"hadronic": True,
"semileptonic": False,
"KLong": False,
"baryonic": True
}
[docs]
def build_lists(self, path):
ma.cutAndCopyList("B0:feiHadronic", "B0:generic",
"Mbc > 5.2 and abs(deltaE) < 0.3 and [sigProb > 0.001 or extraInfo(dmID)==23]", path=path)
ma.cutAndCopyList("B+:feiHadronic", "B+:generic",
"Mbc > 5.2 and abs(deltaE) < 0.3 and [sigProb > 0.001 or extraInfo(dmID)==25]", path=path)
return ["B0:feiHadronic", "B+:feiHadronic"]
[docs]
@_FEI_skim_header(["B0", "B+"])
class feiHadronic_DstEllNu(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`M_{\\text{bc}} > 5.27~{\\rm GeV}`
* :math:`-0.150 < \\Delta E < 0.100~{\\rm GeV}`
* :math:`\\text{signal probability} > 0.001` (omitted for decay mode 25 for
:math:`B^+`, and decay mode 23 for :math:`B^0`)
* :math:`\\cos{TBTO} < 0.9`
* Selects only the two best candidates that survive based on the signalProbability
All available FEI :math:`B^0` and :math:`B^+` hadronic tags are reconstructed. From
`Thomas Keck's thesis
<https://docs.belle2.org/record/275/files/BELLE2-MTHESIS-2015-001.pdf>`_, "the
channel :math:`B^0 \\to \\overline{D}^0 \\pi^0` was used by the FR, but is not yet
used in the FEI due to unexpected technical restrictions in the KFitter algorithm".
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = ("FEI-tagged neutral and charged :math:`B`'s decaying hadronically. "
"Analysis specific cuts applied during skimming. Best 2 candidates ranked by sigProb kept"
)
FEIChannelArgs = {
"neutralB": True,
"chargedB": True,
"hadronic": True,
"semileptonic": False,
"KLong": False,
"baryonic": True,
}
[docs]
def build_lists(self, path):
ma.cutAndCopyList("B0:feiHadronicDstEllNu", "B0:generic", "sigProb > 0.001 or extraInfo(dmID)==23", path=path)
ma.cutAndCopyList("B+:feiHadronicDstEllNu", "B+:generic", "sigProb > 0.001 or extraInfo(dmID)==25", path=path)
HadronicBLists = ["B0:feiHadronicDstEllNu", "B+:feiHadronicDstEllNu"]
for BList in HadronicBLists:
ma.applyCuts(BList, "Mbc > 5.27 and -0.15 <= deltaE <= 0.10", path=path)
# Need to build Btag_ROE to build continuum suppression variables i.e. cosTBTO
self._build_continuum_suppression(particle_list=BList, path=path)
ma.applyCuts(BList, "cosTBTO < 0.9", path=path)
# Keep only the best 2 candidates that survive based on the Signal probability.
# The second candidate is kept just in case an analyst wishes to
# perform some tag side studies
ma.rankByHighest(
particleList=BList,
variable="extraInfo(SignalProbability)",
numBest=2,
path=path,
)
return HadronicBLists
def _build_continuum_suppression(self, particle_list: str, path: b2.Path):
"""Builds continuum suppression for a given b-meson list.
This module is required to save the CS variables.
Args:2
list_name (str) : name of the b meson list. Can be list of signal or tag b meson lists
path (b2.Path): the basf2 path to append modules
"""
mask_name = "btag_cs"
# First build the rest of the event for the b meson
ma.buildRestOfEvent(target_list_name=particle_list, path=path)
# Append the ROE mask
ma.appendROEMasks(
particle_list,
[self._build_btag_roe_mask(mask_name=mask_name)],
path=path,
)
# Build the continuum object for the given b meson
ma.buildContinuumSuppression(
list_name=particle_list, roe_mask=mask_name, path=path
)
@staticmethod
def _build_btag_roe_mask(mask_name: str):
"""Prepares a tuple with the ROE mask name and the ROE cuts for tracks and clusters
The actual cuts are read from a selections dictionary
The cuts are aligning with the continuum rejection that is applied for the centrally
produced FEI calibration
Args:
mask_name (str): The name of the mask. This is the name that will be used to append the mask
Return:
tuple of mask_name, tracking cuts and cluster_cuts
"""
selections = {
"tracks": [
"abs(dr) < 2",
"abs(dz) < 4",
"pt > 0.2",
"thetaInCDCAcceptance == 1",
],
"clusters": [
"clusterNHits > 1.5",
"[[clusterReg==1 and E > 0.080] or [clusterReg==2 and E > 0.030] or [clusterReg==3 and E > 0.060]]",
"thetaInCDCAcceptance",
"abs(clusterTiming) < 200",
],
}
track_cuts = "".join(["[", " and ".join(selections["tracks"]), "]"])
cluster_cuts = "".join(["[", " and ".join(selections["clusters"]), "]"])
return (mask_name, track_cuts, cluster_cuts)
[docs]
@_FEI_skim_header(["B0", "B+"])
class feiSL(BaseFEISkim):
"""
Tag side :math:`B` cuts:
* :math:`-4 < \\cos\\theta_{BY} < 3`
* :math:`\\log_{10}(\\text{signal probability}) > -2.4`
* :math:`p_{\\ell}^{*} > 1.0~{\\rm GeV}` in CMS frame
SL :math:`B^0` and :math:`B^+` tags are reconstructed. Hadronic :math:`B` with SL
:math:`D` are not reconstructed, as these are rare and time-intensive.
<CHANNELS>
See also:
`BaseFEISkim.FEIPrefix` for FEI training used, and `BaseFEISkim.fei_precuts` for
event-level cuts made before applying the FEI.
"""
__description__ = "FEI-tagged neutral and charged :math:`B`'s decaying semileptonically."
FEIChannelArgs = {
"neutralB": True,
"chargedB": True,
"hadronic": False,
"semileptonic": True,
"KLong": False,
"baryonic": True,
"removeSLD": True
}
[docs]
def build_lists(self, path):
Bcuts = " and ".join(["log10_sigProb > -2.4", "-4 < cosThetaBY < 3", "p_lepton_CMSframe > 1"])
ma.cutAndCopyList("B0:feiSL", "B0:semileptonic", Bcuts, path=path)
ma.cutAndCopyList("B+:feiSL", "B+:semileptonic", Bcuts, path=path)
return ["B0:feiSL", "B+:feiSL"]
