ModularAnalysis

7.2.1. ModularAnalysis#

This module defines wrapper functions around the analysis modules.

modularAnalysis.addInclusiveDstarReconstruction(decayString, slowPionCut, DstarCut, path)[source]#

Adds the InclusiveDstarReconstruction module to the given path. This module creates a D* particle list by estimating the D* four momenta from slow pions, specified by a given cut. The D* energy is approximated as E(D*) = m(D*)/(m(D*) - m(D)) * E(pi). The absolute value of the D* momentum is calculated using the D* PDG mass and the direction is collinear to the slow pion direction. The charge of the given pion list has to be consistent with the D* charge

Parameters:

decayString – Decay string, must be of form D* -> pi
slowPionCut – Cut applied to the input pion list to identify slow pions
DstarCut – Cut applied to the output D* list
path – the module is added to this path

modularAnalysis.addPhotonEfficiencyRatioVariables(inputListNames, tableName, path=None)[source]#

Add photon Data/MC detection efficiency ratio weights to the specified particle list

Parameters:

inputListNames (list(str)) – input particle list names
tableName – taken from database with appropriate name
path (basf2.Path) – module is added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.addPi0VetoEfficiencySystematics(particleList, decayString, tableName, threshold, mode='standard', suffix='', path=None)[source]#

Add pi0 veto Data/MC efficiency ratio weights to the specified particle list

Parameters:

particleList – the input ParticleList
decayString – specify hard photon to be performed pi0 veto (e.g. ‘B+:sig -> rho+:sig ^gamma:hard’)
tableName – table name corresponding to payload version (e.g. ‘Pi0VetoEfficiencySystematics_Mar2022’)
threshold – pi0 veto threshold (0.10, 0.11, …, 0.99)
mode – choose one mode (same as writePi0EtaVeto) out of ‘standard’, ‘tight’, ‘cluster’ and ‘both’
suffix – optional suffix to be appended to the usual extraInfo name
path – the module is added to this path

The following extraInfo are available related with the given particleList:

Pi0VetoEfficiencySystematics_{mode}{suffix}_data_MC_ratio : weight of Data/MC for the veto efficiency
Pi0VetoEfficiencySystematics_{mode}{suffix}_data_MC_uncertainty_stat : the statistical uncertainty of the weight
Pi0VetoEfficiencySystematics_{mode}{suffix}_data_MC_uncertainty_sys : the systematic uncertainty of the weight
Pi0VetoEfficiencySystematics_{mode}{suffix}_data_MC_uncertainty_total : the total uncertainty of the weight
Pi0VetoEfficiencySystematics_{mode}{suffix}_threshold : threshold of the pi0 veto

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.appendROEMask(list_name, mask_name, trackSelection, eclClusterSelection, klmClusterSelection='', path=None)[source]#

Loads the ROE object of a particle and creates a ROE mask with a specific name. It applies selection criteria for tracks and eclClusters which will be used by variables in ROEVariables.cc.

append a ROE mask with all tracks in ROE coming from the IP region

appendROEMask('B+:sig', 'IPtracks', '[dr < 2] and [abs(dz) < 5]', path=mypath)

append a ROE mask with only ECL-based particles that pass as good photon candidates

goodPhotons = 'inCDCAcceptance and clusterErrorTiming < 1e6 and [clusterE1E9 > 0.4 or E > 0.075]'
appendROEMask('B+:sig', 'goodROEGamma', '', goodPhotons, path=mypath)

Parameters:

list_name – name of the input ParticleList
mask_name – name of the appended ROEMask
trackSelection – decay string for the track-based particles in ROE
eclClusterSelection – decay string for the ECL-based particles in ROE
klmClusterSelection – decay string for the KLM-based particles in ROE
path – modules are added to this path

modularAnalysis.appendROEMasks(list_name, mask_tuples, path=None)[source]#

Loads the ROE object of a particle and creates a ROE mask with a specific name. It applies selection criteria for track-, ECL- and KLM-based particles which will be used by ROE variables.

The multiple ROE masks with their own selection criteria are specified via list of tuples (mask_name, trackParticleSelection, eclParticleSelection, klmParticleSelection) or (mask_name, trackSelection, eclClusterSelection) in case with fractions.

Example for two tuples, one with and one without fractions

ipTracks     = ('IPtracks', '[dr < 2] and [abs(dz) < 5]', '', '')
goodPhotons = 'inCDCAcceptance and [clusterErrorTiming < 1e6] and [clusterE1E9 > 0.4 or E > 0.075]'
goodROEGamma = ('ROESel', '[dr < 2] and [abs(dz) < 5]', goodPhotons, '')
goodROEKLM     = ('IPtracks', '[dr < 2] and [abs(dz) < 5]', '', 'nKLMClusterTrackMatches == 0')
appendROEMasks('B+:sig', [ipTracks, goodROEGamma, goodROEKLM], path=mypath)

Parameters:

list_name – name of the input ParticleList
mask_tuples – array of ROEMask list tuples to be appended
path – modules are added to this path

modularAnalysis.applyChargedPidMVA(particleLists, path, trainingMode, chargeIndependent=False, binaryHypoPDGCodes=(0, 0))[source]#

Use an MVA to perform particle identification for charged stable particles, using the ChargedPidMVA module.

The module decorates Particle objects in the input ParticleList(s) with variables containing the appropriate MVA score, which can be used to select candidates by placing a cut on it.

Note

The MVA algorithm used is a gradient boosted decision tree (TMVA 4.3.0, ROOT 6.20/04).

The module can perform either ‘binary’ PID between input S, B particle mass hypotheses according to the following scheme:

e (11) vs. pi (211)
mu (13) vs. pi (211)
pi (211) vs. K (321)
K (321) vs. pi (211)

, or ‘global’ PID, namely “one-vs-others” separation. The latter exploits an MVA algorithm trained in multi-class mode, and it’s the default behaviour. Currently, the multi-class training separates the following standard charged hypotheses:

e (11), mu (13), pi (211), K (321)

Warning

In order to run the ChargedPidMVA and ensure the most up-to-date MVA training weights are applied, it is necessary to append the latest analysis global tag (GT) to the steering script.

Parameters:

particleLists (list(str)) – the input list of DecayStrings, where each selected (^) daughter should correspond to a standard charged ParticleList, e.g. ['Lambda0:sig -> ^p+ ^pi-', 'J/psi:sig -> ^mu+ ^mu-']. One can also directly pass a list of standard charged ParticleLists, e.g. ['e+:my_electrons', 'pi+:my_pions']. Note that charge-conjugated ParticleLists will automatically be included.
path (basf2.Path) – the module is added to this path.
trainingMode (Belle2.ChargedPidMVAWeights.ChargedPidMVATrainingMode) –
enum identifier of the training mode. Needed to pick up the correct payload from the DB. Available choices:
- c_Classification=0
- c_Multiclass=1
- c_ECL_Classification=2
- c_ECL_Multiclass=3
- c_PSD_Classification=4
- c_PSD_Multiclass=5
- c_ECL_PSD_Classification=6
- c_ECL_PSD_Multiclass=7
chargeIndependent (bool, optional) – use a BDT trained on a sample of inclusively charged particles.
binaryHypoPDGCodes (tuple(int, int), optional) – the pdgIds of the signal, background mass hypothesis. Required only for binary PID mode.

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.applyCuts(list_name, cut, path)[source]#

Removes particle candidates from list_name that do not pass cut (given selection criteria).

Example

require energetic pions safely inside the cdc

applyCuts("pi+:mypions", "[E > 2] and thetaInCDCAcceptance", path=mypath)

Warning

You must use square braces [ and ] for conditional statements.

Parameters:

list_name (str) – input ParticleList name
cut (str) – Candidates that do not pass these selection criteria are removed from the ParticleList
path (basf2.Path) – modules are added to this path

modularAnalysis.applyEventCuts(cut, path, metavariables=None)[source]#

Removes events that do not pass the cut (given selection criteria).

Example

continuum events (in mc only) with more than 5 tracks

applyEventCuts("[nTracks > 5] and [isContinuumEvent], path=mypath)

Warning

Only event-based variables are allowed in this function and only square brackets [ and ] for conditional statements.

Parameters:

cut (str) – Events that do not pass these selection criteria are skipped
path (basf2.Path) – modules are added to this path
metavariables (list(str)) – List of meta variables to be considered in decomposition of cut

modularAnalysis.applyRandomCandidateSelection(particleList, path=None)[source]#

If there are multiple candidates in the provided particleList, all but one of them are removed randomly. This is done on a event-by-event basis.

Parameters:

particleList – ParticleList for which the random candidate selection should be applied
path – module is added to this path

modularAnalysis.buildContinuumSuppression(list_name, roe_mask, path)[source]#

Creates for each Particle in the given ParticleList a ContinuumSuppression dataobject and makes basf2 relation between them.

Parameters:

list_name – name of the input ParticleList
roe_mask – name of the ROE mask
path – modules are added to this path

modularAnalysis.buildEventKinematics(inputListNames=None, default_cleanup=True, custom_cuts=None, chargedPIDPriors=None, fillWithMostLikely=False, path=None)[source]#

Calculates the global kinematics of the event (visible energy, missing momentum, missing mass…) using ParticleLists provided. If no ParticleList is provided, default ParticleLists are used (all track and all hits in ECL without associated track).

The visible energy missing values are stored in a EventKinematics dataobject.

Parameters:

inputListNames – list of ParticleLists used to calculate the global event kinematics. If the list is empty, default ParticleLists pi+:evtkin and gamma:evtkin are filled.
fillWithMostLikely – if True, the module uses the most likely particle mass hypothesis for charged particles according to the PID likelihood and the option inputListNames will be ignored.
chargedPIDPriors – The prior PID fractions, that are used to regulate amount of certain charged particle species, should be a list of six floats if not None. The order of particle types is the following: [e-, mu-, pi-, K-, p+, d+]
default_cleanup – if True and either inputListNames empty or fillWithMostLikely True, default clean up cuts are applied
custom_cuts – tuple of selection cut strings of form (trackCuts, photonCuts), default is None, which would result in a standard predefined selection cuts
path – modules are added to this path

modularAnalysis.buildEventKinematicsFromMC(inputListNames=None, selectionCut='', path=None)[source]#

Calculates the global kinematics of the event (visible energy, missing momentum, missing mass…) using generated particles. If no ParticleList is provided, default generated ParticleLists are used.

Parameters:

inputListNames – list of ParticleLists used to calculate the global event kinematics. If the list is empty, default ParticleLists are filled.
selectionCut – optional selection cuts
path – Path to append the eventKinematics module to.

modularAnalysis.buildEventShape(inputListNames=None, default_cleanup=True, custom_cuts=None, allMoments=False, cleoCones=True, collisionAxis=True, foxWolfram=True, harmonicMoments=True, jets=True, sphericity=True, thrust=True, checkForDuplicates=False, path=None)[source]#

Calculates the event-level shape quantities (thrust, sphericity, Fox-Wolfram moments…) using the particles in the lists provided by the user. If no particle list is provided, the function will internally create a list of good tracks and a list of good photons with (optionally) minimal quality cuts.

The results of the calculation are then stored into the EventShapeContainer dataobject, and are accessible using the variables of the EventShape group.

The user can switch the calculation of certain quantities on or off to save computing time. By default the calculation of the high-order moments (5-8) is turned off. Switching off an option will make the corresponding variables not available.

Warning

The user can provide as many particle lists as needed, using also combined particles, but the function will always assume that the lists are independent. If the lists provided by the user contain several times the same track (either with different mass hypothesis, or once as an independent particle and once as daughter of a combined particle) the results won’t be reliable. A basic check for duplicates is available setting the checkForDuplicate flags.

Parameters:

inputListNames – List of ParticleLists used to calculate the event shape variables. If the list is empty the default particleLists pi+:evtshape and gamma:evtshape are filled.
default_cleanup – If True, applies standard cuts on pt and cosTheta when defining the internal lists. This option is ignored if the particleLists are provided by the user.
custom_cuts – tuple of selection cut strings of form (trackCuts, photonCuts), default is None, which would result in a standard predefined selection cuts
path – Path to append the eventShape modules to.
thrust – Enables the calculation of thrust-related quantities (CLEO cones, Harmonic moments, jets).
collisionAxis – Enables the calculation of the quantities related to the collision axis .
foxWolfram – Enables the calculation of the Fox-Wolfram moments.
harmonicMoments – Enables the calculation of the Harmonic moments with respect to both the thrust axis and, if collisionAxis = True, the collision axis.
allMoments – If True, calculates also the FW and harmonic moments from order 5 to 8 instead of the low-order ones only.
cleoCones – Enables the calculation of the CLEO cones with respect to both the thrust axis and, if collisionAxis = True, the collision axis.
jets – Enables the calculation of the hemisphere momenta and masses. Requires thrust = True.
sphericity – Enables the calculation of the sphericity-related quantities.
checkForDuplicates – Perform a check for duplicate particles before adding them. Regardless of the value of this option, it is recommended to consider sanitizing the lists you are passing to the function.

modularAnalysis.buildNestedRestOfEvent(target_list_name, maskName='all', path=None)[source]#

Creates for each Particle in the given ParticleList a RestOfEvent

Parameters:

target_list_name – name of the input ParticleList
mask_name – name of the ROEMask to be used
path – modules are added to this path

modularAnalysis.buildRestOfEvent(target_list_name, inputParticlelists=None, fillWithMostLikely=True, chargedPIDPriors=None, path=None)[source]#

Creates for each Particle in the given ParticleList a RestOfEvent dataobject and makes basf2 relation between them. User can provide additional particle lists with a different particle hypothesis like [‘K+:good, e+:good’], etc.

Parameters:

target_list_name – name of the input ParticleList
inputParticlelists – list of user-defined input particle list names, which serve as source of particles to build the ROE, the FSP particles from target_list_name are automatically excluded from the ROE object
fillWithMostLikely – By default the module uses the most likely particle mass hypothesis for charged particles based on the PID likelihood. Turn this behavior off if you want to configure your own input particle lists.
chargedPIDPriors – The prior PID fractions, that are used to regulate the amount of certain charged particle species, should be a list of six floats if not None. The order of particle types is the following: [e-, mu-, pi-, K-, p+, d+]
path – modules are added to this path

modularAnalysis.buildRestOfEventFromMC(target_list_name, inputParticlelists=None, path=None)[source]#

Creates for each Particle in the given ParticleList a RestOfEvent

Parameters:

target_list_name – name of the input ParticleList
inputParticlelists – list of input particle list names, which serve as a source of particles to build ROE, the FSP particles from target_list_name are excluded from ROE object
path – modules are added to this path

modularAnalysis.calculateDistance(list_name, decay_string, mode='vertextrack', path=None)[source]#

Calculates distance between two vertices, distance of closest approach between a vertex and a track, distance of closest approach between a vertex and btube. For track, this calculation ignores track curvature, it’s negligible for small distances.The user should use extraInfo(CalculatedDistance) to get it. A full example steering file is at analysis/tests/test_DistanceCalculator.py

Example

from modularAnalysis import calculateDistance
calculateDistance('list_name', 'decay_string', "mode", path=user_path)

Parameters:

list_name – name of the input ParticleList
decay_string – select particles between the distance of closest approach will be calculated
mode – Specifies how the distance is calculated vertextrack: calculate the distance of closest approach between a track and a vertex, taking the first candidate as vertex, default trackvertex: calculate the distance of closest approach between a track and a vertex, taking the first candidate as track 2tracks: calculates the distance of closest approach between two tracks 2vertices: calculates the distance between two vertices vertexbtube: calculates the distance of closest approach between a vertex and btube trackbtube: calculates the distance of closest approach between a track and btube
path – modules are added to this path

modularAnalysis.calculateTrackIsolation(decay_string, path, *detectors, reference_list_name=None, vars_for_nearest_part=[], highest_prob_mass_for_ext=True, exclude_pid_det_weights=False)[source]#

Given an input decay string, compute variables that quantify track helix-based isolation of the charged stable particles in the input decay chain.

Note

An “isolation score” can be defined using the distance of each particle to its closest neighbour, defined as the segment connecting the two extrapolated track helices intersection points on a given cylindrical surface. The distance variables defined in the VariableManager is named minET2ETDist, the isolation scores are named minET2ETIsoScore, minET2ETIsoScoreAsWeightedAvg.

The definition of distance and the number of distances that are calculated per sub-detector is based on the following recipe:

CDC: as the segmentation is very coarse along $z$, the distance is defined as the cord length on the $(\rho=R, \phi)$ plane. A total of 9 distances are calculated: the cylindrical surfaces are defined at radiuses that correspond to the positions of the 9 CDC wire superlayers: $R_{i}^{\mathrm{CDC}}~(i \in \{0,...,8\})$.
TOP: as there is no segmentation along $z$, the distance is defined as the cord length on the $(\rho=R, \phi)$ plane. Only one distance at the TOP entry radius $R_{0}^{\mathrm{TOP}}$ is calculated.
ARICH: as there is no segmentation along $z$, the distance is defined as the distance on the $(\rho=R, \phi)$ plane at fixed $z=Z$. Only one distance at the ARICH photon detector entry coordinate $Z_{0}^{\mathrm{ARICH}}$ is calculated.
ECL: the distance is defined on the $(\rho=R, \phi, z)$ surface in the barrel, on the $(\rho, \phi, z=Z)$ surface in the endcaps. Two distances are calculated: one at the ECL entry surface $R_{0}^{\mathrm{ECL}}$ (barrel), $Z_{0}^{\mathrm{ECL}}$ (endcaps), and one at $R_{1}^{\mathrm{ECL}}$ (barrel), $Z_{1}^{\mathrm{ECL}}$ (endcaps), corresponding roughly to the mid-point of the longitudinal size of the crystals.
KLM: the distance is defined on the $(\rho=R, \phi, z)$ surface in the barrel, on the $(\rho, \phi, z=Z)$ surface in the endcaps. Only one distance at the KLM first strip entry surface $R_{0}^{\mathrm{KLM}}$ (barrel), $Z_{0}^{\mathrm{KLM}}$ (endcaps) is calculated.

Parameters:

decay_string (str) – name of the input decay string with selected charged stable daughters, for example: Lambda0:merged -> ^p+ ^pi-. Alternatively, it can be a particle list for charged stable particles as defined in Const::chargedStableSet, for example: mu+:all. The charge-conjugate particle list will be also processed automatically.
path (basf2.Path) – path to which module(s) will be added.
*detectors – detectors for which track isolation variables will be calculated. Choose among: {'CDC', 'TOP', 'ARICH', 'ECL', 'KLM'}.
reference_list_name (Optional[str]) – name of the input charged stable particle list for the reference tracks. By default, the :all ParticleList of the same type of the selected particle in decay_string is used. The charge-conjugate particle list will be also processed automatically.
vars_for_nearest_part (Optional[list(str)]) – a list of variables to calculate for the nearest particle in the reference list at each detector surface. It uses the metavariable minET2ETDistVar. If unset, only the distances to the nearest neighbour per detector are calculated.
highest_prob_mass_for_hex (Optional[bool]) – if this option is set to True (default), the helix extrapolation for the particles will use the track fit result for the most probable mass hypothesis, namely, the one that gives the highest chi2Prob of the fit. Otherwise, it uses the mass hypothesis that corresponds to the particle lists PDG.
exclude_pid_det_weights (Optional[bool]) – if this option is set to False (default), the isolation score calculation will take into account the weight that each detector has on the PID for the particle species of interest.

Returns:

a dictionary mapping the PDG of each reference particle list to its isolation variables.

Return type:

dict(int, list(str))

modularAnalysis.combineAllParticles(inputParticleLists, outputList, cut='', writeOut=False, path=None)[source]#

Creates a new Particle as the combination of all Particles from all provided inputParticleLists. However, each particle is used only once (even if duplicates are provided) and the combination has to pass the specified selection criteria to be saved in the newly created (mother) ParticleList.

Parameters:

inputParticleLists – List of input particle lists which are combined to the new Particle
outputList – Name of the particle combination created with this module
cut – created (mother) Particle is added to the mother ParticleList if it passes these given cuts (in VariableManager style) and is rejected otherwise
writeOut – whether RootOutput module should save the created ParticleList
path – module is added to this path

modularAnalysis.copyList(outputListName, inputListName, writeOut=False, path=None)[source]#

Copy all Particle indices from input ParticleList to the output ParticleList. Note that the Particles themselves are not copied. The original and copied ParticleLists will point to the same Particles.

Parameters:

ouputListName – copied ParticleList
inputListName – original ParticleList to be copied
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path

modularAnalysis.copyLists(outputListName, inputListNames, writeOut=False, path=None)[source]#

Copy all Particle indices from all input ParticleLists to the single output ParticleList. Note that the Particles themselves are not copied. The original and copied ParticleLists will point to the same Particles.

Duplicates are removed based on the first-come, first-served principle. Therefore, the order of the input ParticleLists matters.

See also

If you want to select the best duplicate based on another criterion, have a look at the function mergeListsWithBestDuplicate.

Note

Two particles that differ only by the order of their daughters are considered duplicates and one of them will be removed.

Parameters:

ouputListName – copied ParticleList
inputListName – vector of original ParticleLists to be copied
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path

modularAnalysis.copyParticles(outputListName, inputListName, writeOut=False, path=None)[source]#

Create copies of Particles given in the input ParticleList and add them to the output ParticleList.

The existing relations of the original Particle (or it’s (grand-)^n-daughters) are copied as well. Note that only the relation is copied and that the related object is not. Copied particles are therefore related to the same object as the original ones.

Parameters:

ouputListName – new ParticleList filled with copied Particles
inputListName – input ParticleList with original Particles
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path

modularAnalysis.correctBrems(outputList, inputList, gammaList, maximumAcceptance=3.0, multiplePhotons=False, usePhotonOnlyOnce=True, writeOut=False, path=None)[source]#

For each particle in the given inputList, copies it to the outputList and adds the 4-vector of the photon(s) in the gammaList which has(have) a weighted named relation to the particle’s track, set by the ECLTrackBremFinder module during reconstruction.

Warning

This can only work if the mdst file contains the Bremsstrahlung named relation. Official MC samples up to and including MC12 and proc9 do not contain this. Newer production campaigns (from proc10 and MC13) do. However, studies by the tau WG revealed that the cuts applied by the ECLTrackBremFinder module are too tight. These will be loosened but this will only have effect with proc13 and MC15. If your analysis is very sensitive to the Bremsstrahlung corrections, it is advised to use correctBremsBelle.

Information:

A detailed description of how the weights are set can be found directly at the documentation of the BremsFinder module.

Please note that a new particle is always generated, with the old particle and -if found- one or more photons as daughters.

The inputList should contain particles with associated tracks. Otherwise, the module will exit with an error.

The gammaList should contain photons. Otherwise, the module will exit with an error.

Parameters:

outputList – The output particle list name containing the corrected particles
inputList – The initial particle list name containing the particles to correct. It should already exist.
gammaList – The photon list containing possibly bremsstrahlung photons; It should already exist.
maximumAcceptance – Maximum value of the relation weight. Should be a number between [0,3)
multiplePhotons – Whether to use only one photon (the one with the smallest acceptance) or as many as possible
usePhotonOnlyOnce – If true, each brems candidate is used to correct only the track with the smallest relation weight
writeOut – Whether RootOutput module should save the created outputList
path – The module is added to this path

modularAnalysis.correctBremsBelle(outputListName, inputListName, gammaListName, multiplePhotons=True, angleThreshold=0.05, usePhotonOnlyOnce=False, writeOut=False, path=None)[source]#

Run the Belle - like brems finding on the inputListName of charged particles. Adds all photons in gammaListName to a copy of the charged particle that are within angleThreshold.

Tip

Studies by the tau WG show that using a rather wide opening angle (up to 0.2 rad) and rather low energetic photons results in good correction. However, this should only serve as a starting point for your own studies because the optimal criteria are likely mode-dependent

Parameters:

outputListName (str) – The output charged particle list containing the corrected charged particles
inputListName (str) – The initial charged particle list containing the charged particles to correct.
gammaListName (str) – The gammas list containing possibly radiative gammas, should already exist.
multiplePhotons (bool) – How many photons should be added to the charged particle? nearest one -> False, add all the photons within the cone -> True
angleThreshold (float) – The maximum angle in radians between the charged particle and the (radiative) gamma to be accepted.
writeOut (bool) – whether RootOutput module should save the created ParticleList
usePhotonOnlyOnce (bool) – If true, a photon is used for correction of the closest charged particle in the inputList. If false, a photon is allowed to be used for correction multiple times (Default).
Warning – One cannot use a photon twice to reconstruct a composite particle. Thus, for example, if e+ and e- are corrected with a gamma, the pair of e+ and e- cannot form a J/psi -> e+ e- candidate.
path (basf2.Path) – modules are added to this path

modularAnalysis.correctEnergyBias(inputListNames, tableName, path=None)[source]#

Scale energy of the particles according to the scaling factor. If the particle list contains composite particles, the energy of the daughters are scaled. Subsequently, the energy of the mother particle is updated as well.

Parameters:

inputListNames (list(str)) – input particle list names
tableName – stored in localdb and created using ParticleWeightingLookUpCreator
path (basf2.Path) – module is added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.correctTrackEnergy(inputListNames, correction=nan, payloadName='', correctionName='SF', path=None)[source]#

Correct the energy loss of tracks according to a ‘correction’ value. This correction can either be given as constant number or as the name of the payload which contains the variable corrections. If the particle list contains composite particles, the momenta of the track-based daughters are corrected. Subsequently, the momentum of the mother particle is updated as well.

Parameters:

inputListNames (list(str)) – input particle list names
correction (float) – correction value to be subtracted to the particle energy (0.0 – no correction)
payloadName (str) – name of the payload which contains the phase-space dependent scaling factors
correctionName (str) – name of correction variable in the payload.
path (basf2.Path) – module is added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.cutAndCopyList(outputListName, inputListName, cut, writeOut=False, path=None)[source]#

Copy candidates from inputListName to outputListName if they pass cut (given selection criteria).

Note

Note the Particles themselves are not copied. The original and copied ParticleLists will point to the same Particles.

Example

require energetic pions safely inside the cdc

cutAndCopyList("pi+:energeticPions", "pi+:loose", "[E > 2] and thetaInCDCAcceptance", path=mypath)

Warning

You must use square braces [ and ] for conditional statements.

Parameters:

outputListName (str) – the new ParticleList name
inputListName (str) – input ParticleList name
cut (str) – Candidates that do not pass these selection criteria are removed from the ParticleList
writeOut (bool) – whether RootOutput module should save the created ParticleList
path (basf2.Path) – modules are added to this path

modularAnalysis.cutAndCopyLists(outputListName, inputListNames, cut, writeOut=False, path=None)[source]#

Copy candidates from all lists in inputListNames to outputListName if they pass cut (given selection criteria).

Note

Note that the Particles themselves are not copied. The original and copied ParticleLists will point to the same Particles.

Example

Require energetic pions safely inside the cdc

cutAndCopyLists("pi+:energeticPions", ["pi+:good", "pi+:loose"], "[E > 2] and thetaInCDCAcceptance", path=mypath)

Warning

You must use square braces [ and ] for conditional statements.

Parameters:

outputListName (str) – the new ParticleList name
inputListName (list(str)) – list of input ParticleList names
cut (str) – Candidates that do not pass these selection criteria are removed from the ParticleList
writeOut (bool) – whether RootOutput module should save the created ParticleList
path (basf2.Path) – modules are added to this path

modularAnalysis.discardFromROEMasks(list_name, mask_names, cut_string, path=None)[source]#

This function is used to apply particle list specific cuts on one or more ROE masks (track or eclCluster). With this function one can DISCARD the tracks/eclclusters used in particles from provided particle list. This function should be executed only in the for_each roe path for the current ROE object.

To avoid unnecessary computation, the input particle list should only contain particles from ROE (use cut ‘isInRestOfEvent == 1’). To update the ECLCluster masks, the input particle list should be a photon particle list (e.g. ‘gamma:someLabel’). To update the Track masks, the input particle list should be a charged pion particle list (e.g. ‘pi+:someLabel’).

Updating a non-existing mask will create a new one.

discard tracks that were used in provided particle list

discardFromROEMasks('pi+:badTracks', 'mask', '', path=mypath)

discard clusters that were used in provided particle list and pass a cut, apply to several masks

discardFromROEMasks('gamma:badClusters', ['mask1', 'mask2'], 'E < 0.1', path=mypath)

Parameters:

list_name – name of the input ParticleList
mask_names – array of ROEMasks to be updated
cut_string – decay string with which the mask will be updated
path – modules are added to this path

modularAnalysis.estimateAndAttachTrackFitResult(inputListName, path=None)[source]#

Create a TrackFitResult from the momentum of the Particle assuming it originates from the IP and make a relation between them. The covariance, detector hit information, and fit-related information (pValue, NDF) are assigned meaningless values. The input Particles must not have already Track or TrackFitResult and thus are supposed to be composite particles, recoil, dummy particles, and so on.

Warning

Since the source type is not overwritten as Track, not all track-related variables are guaranteed to be available.

Parameters:: inputListName – Name of input ParticleList

modularAnalysis.extractParticlesFromROE(particleLists, signalSideParticleList=None, maskName='all', writeOut=False, path=None)[source]#

Extract Particle objects that belong to the Rest-Of-Events and fill them into the ParticleLists. The types of the particles other than those specified by particleLists are not stored. If one creates a ROE with fillWithMostLikely=True via buildRestOfEvent, for example, one should create particleLists for not only pi+, gamma, K_L0 but also other charged final state particles.

When one calls the function in the main path, one has to set the argument signalSideParticleList and the signal side ParticleList must have only one candidate.

buildRestOfEvent('B0:sig', fillWithMostLikely=True, path=mypath)

roe_path = create_path()
deadEndPath = create_path()
signalSideParticleFilter('B0:sig', '', roe_path, deadEndPath)

plists = ['%s:in_roe' % ptype for ptype in ['pi+', 'gamma', 'K_L0', 'K+', 'p+', 'e+', 'mu+']]
extractParticlesFromROE(plists, maskName='all', path=roe_path)

# one can analyze these ParticleLists in the roe_path

mypath.for_each('RestOfEvent', 'RestOfEvents', roe_path)

rankByLowest('B0:sig', 'deltaE', numBest=1, path=mypath)
extractParticlesFromROE(plists, signalSideParticleList='B0:sig', maskName='all', path=mypath)

# one can analyze these ParticleLists in the main path

Parameters:

particleLists – (str or list(str)) Name of output ParticleLists
signalSideParticleList – (str) Name of signal side ParticleList
maskName – (str) Name of the ROE mask to be applied on Particles
writeOut – (bool) whether RootOutput module should save the created ParticleList
path – (basf2.Path) modules are added to this path

modularAnalysis.fillConvertedPhotonsList(decayString, cut, writeOut=False, path=None)[source]#

Creates photon Particle object for each e+e- combination in the V0 StoreArray.

Note

You must specify the daughter ordering.

fillConvertedPhotonsList('gamma:converted -> e+ e-', '', path=mypath)

Parameters:

decayString (str) – Must be gamma to an e+e- pair. You must specify the daughter ordering. Will also determine the name of the particleList.
cut (str) – Particles need to pass these selection criteria to be added to the ParticleList
writeOut (bool) – whether RootOutput module should save the created ParticleList
path (basf2.Path) – modules are added to this path

modularAnalysis.fillParticleList(decayString, cut, writeOut=False, path=None, enforceFitHypothesis=False, loadPhotonsFromKLM=False)[source]#

Creates Particles of the desired type from the corresponding mdst dataobjects, loads them to the StoreArray<Particle> and fills the ParticleList.

See also

the Standard Particles functions.

The type of the particles to be loaded is specified via the decayString module parameter. The type of the mdst dataobject that is used as an input is determined from the type of the particle. The following types of the particles can be loaded:

charged final state particles (input mdst type = Tracks)
- e+, mu+, pi+, K+, p, deuteron (and charge conjugated particles)
neutral final state particles
- “gamma” (input mdst type = ECLCluster)
- “K_S0”, “Lambda0” (input mdst type = V0)
- “K_L0” (input mdst type = KLMCluster or ECLCluster)

Note

For “K_S0” and “Lambda0” you must specify the daughter ordering.

For example, to load V0s as $\Lambda^0\to p^+\pi^-$ decays from V0s:

fillParticleList('Lambda0 -> p+ pi-', '0.9 < M < 1.3', path=mypath)

Tip

Gammas can also be loaded from KLMClusters by explicitly setting the parameter loadPhotonsFromKLM to True. However, this should only be done in selected use-cases and the effect should be studied carefully.

Tip

For “K_L0” it is now possible to load from ECLClusters, to revert to the old (Belle) behavior, you can require 'isFromKLM > 0'.

fillParticleList('K_L0', 'isFromKLM > 0', path=mypath)

Parameters:

decayString (str) – Type of Particle and determines the name of the ParticleList. If the input MDST type is V0 the whole decay chain needs to be specified, so that the user decides and controls the daughters’ order (e.g. K_S0 -> pi+ pi-)
cut (str) – Particles need to pass these selection criteria to be added to the ParticleList
writeOut (bool) – whether RootOutput module should save the created ParticleList
path (basf2.Path) – modules are added to this path
enforceFitHypothesis (bool) – If true, Particles will be created only for the tracks which have been fitted using a mass hypothesis of the exact type passed to fillParticleLists(). If enforceFitHypothesis is False (the default) the next closest fit hypothesis in terms of mass difference will be used if the fit using exact particle type is not available.
loadPhotonsFromKLM (bool) – If true, photon candidates will be created from KLMClusters as well.

modularAnalysis.fillParticleListFromChargedCluster(outputParticleList, inputParticleList, cut, useOnlyMostEnergeticECLCluster=True, writeOut=False, path=None)[source]#

Creates the Particle object from ECLCluster and KLMCluster that are being matched with the Track of inputParticleList.

Parameters:

outputParticleList – The output ParticleList. Only neutral final state particles are supported.
inputParticleList – The input ParticleList that is required to have the relation to the Track object.
cut – Particles need to pass these selection criteria to be added to the ParticleList
useOnlyMostEnergeticECLCluster – If True, only the most energetic ECLCluster among ones that are matched with the Track is used. If False, all matched ECLClusters are loaded. The default is True. Regardless of this option, the KLMCluster is loaded.
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path

modularAnalysis.fillParticleListFromDummy(decayString, mdstIndex=0, covMatrix=10000.0, treatAsInvisible=True, writeOut=False, path=None)[source]#

Creates a ParticleList and fills it with dummy Particles. For self-conjugated Particles one dummy Particle is created, for Particles that are not self-conjugated one Particle and one anti-Particle is created. The four-momentum is set to zero.

The type of the particles to be loaded is specified via the decayString module parameter.

Parameters:

decayString – specifies type of Particles and determines the name of the ParticleList
mdstIndex – sets the mdst index of Particles
covMatrix – sets the value of the diagonal covariance matrix of Particles
treatAsInvisible – whether treeFitter should treat the Particles as invisible
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path

modularAnalysis.fillParticleListFromMC(decayString, cut, addDaughters=False, skipNonPrimaryDaughters=False, writeOut=False, path=None, skipNonPrimary=False, skipInitial=True)[source]#

Creates Particle object for each MCParticle of the desired type found in the StoreArray<MCParticle>, loads them to the StoreArray<Particle> and fills the ParticleList.

The type of the particles to be loaded is specified via the decayString module parameter.

Parameters:

decayString – specifies type of Particles and determines the name of the ParticleList
cut – Particles need to pass these selection criteria to be added to the ParticleList
addDaughters – adds the bottom part of the decay chain of the particle to the datastore and sets mother-daughter relations
skipNonPrimaryDaughters – if true, skip non primary daughters, useful to study final state daughter particles
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
skipNonPrimary – if true, skip non primary particle
skipInitial – if true, skip initial particles

modularAnalysis.fillParticleListFromROE(decayString, cut, maskName='all', sourceParticleListName='', useMissing=False, writeOut=False, path=None)[source]#

Creates Particle object for each ROE of the desired type found in the StoreArray<RestOfEvent>, loads them to the StoreArray<Particle> and fills the ParticleList. If useMissing is True, then the missing momentum is used instead of ROE.

The type of the particles to be loaded is specified via the decayString module parameter.

Parameters:

decayString – specifies type of Particles and determines the name of the ParticleList. Source ROEs can be taken as a daughter list, for example: ‘B0:tagFromROE -> B0:signal’
cut – Particles need to pass these selection criteria to be added to the ParticleList
maskName – Name of the ROE mask to use
sourceParticleListName – Use related ROEs to this particle list as a source
useMissing – Use missing momentum instead of ROE momentum
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path

modularAnalysis.fillParticleListWithTrackHypothesis(decayString, cut, hypothesis, writeOut=False, enforceFitHypothesis=False, path=None)[source]#

As fillParticleList, but if used for a charged FSP, loads the particle with the requested hypothesis if available

Parameters:

decayString – specifies type of Particles and determines the name of the ParticleList
cut – Particles need to pass these selection criteria to be added to the ParticleList
hypothesis – the PDG code of the desired track hypothesis
writeOut – whether RootOutput module should save the created ParticleList
enforceFitHypothesis – If true, Particles will be created only for the tracks which have been fitted using a mass hypothesis of the exact type passed to fillParticleLists(). If enforceFitHypothesis is False (the default) the next closest fit hypothesis in terms of mass difference will be used if the fit using exact particle type is not available.
path – modules are added to this path

modularAnalysis.fillParticleLists(decayStringsWithCuts, writeOut=False, path=None, enforceFitHypothesis=False, loadPhotonsFromKLM=False)[source]#

Creates Particles of the desired types from the corresponding mdst dataobjects, loads them to the StoreArray<Particle> and fills the ParticleLists.

The multiple ParticleLists with their own selection criteria are specified via list tuples (decayString, cut), for example

kaons = ('K+:mykaons', 'kaonID>0.1')
pions = ('pi+:mypions','pionID>0.1')
fillParticleLists([kaons, pions], path=mypath)

If you are unsure what selection you want, you might like to see the Standard Particles functions.

The type of the particles to be loaded is specified via the decayString module parameter. The type of the mdst dataobject that is used as an input is determined from the type of the particle. The following types of the particles can be loaded:

charged final state particles (input mdst type = Tracks)
- e+, mu+, pi+, K+, p, deuteron (and charge conjugated particles)
neutral final state particles
- “gamma” (input mdst type = ECLCluster)
- “K_S0”, “Lambda0” (input mdst type = V0)
- “K_L0” (input mdst type = KLMCluster or ECLCluster)

Note

For “K_S0” and “Lambda0” you must specify the daughter ordering.

For example, to load V0s as $\Lambda^0\to p^+\pi^-$ decays from V0s:

v0lambdas = ('Lambda0 -> p+ pi-', '0.9 < M < 1.3')
fillParticleLists([kaons, pions, v0lambdas], path=mypath)

Tip

Gammas can also be loaded from KLMClusters by explicitly setting the parameter loadPhotonsFromKLM to True. However, this should only be done in selected use-cases and the effect should be studied carefully.

Tip

For “K_L0” it is now possible to load from ECLClusters, to revert to the old (Belle) behavior, you can require 'isFromKLM > 0'.

klongs = ('K_L0', 'isFromKLM > 0')
fillParticleLists([kaons, pions, klongs], path=mypath)

Parameters:

decayStringsWithCuts (list) – A list of python ntuples of (decayString, cut). The decay string determines the type of Particle and the name of the ParticleList. If the input MDST type is V0 the whole decay chain needs to be specified, so that the user decides and controls the daughters ‘ order (e.g. K_S0 -> pi+ pi-) The cut is the selection criteria to be added to the ParticleList. It can be an empty string.
writeOut (bool) – whether RootOutput module should save the created ParticleList
path (basf2.Path) – modules are added to this path
enforceFitHypothesis (bool) – If true, Particles will be created only for the tracks which have been fitted using a mass hypothesis of the exact type passed to fillParticleLists(). If enforceFitHypothesis is False (the default) the next closest fit hypothesis in terms of mass difference will be used if the fit using exact particle type is not available.
loadPhotonsFromKLM (bool) – If true, photon candidates will be created from KLMClusters as well.

modularAnalysis.fillParticleListsFromMC(decayStringsWithCuts, addDaughters=False, skipNonPrimaryDaughters=False, writeOut=False, path=None, skipNonPrimary=False, skipInitial=True)[source]#

Creates Particle object for each MCParticle of the desired type found in the StoreArray<MCParticle>, loads them to the StoreArray<Particle> and fills the ParticleLists.

The types of the particles to be loaded are specified via the (decayString, cut) tuples given in a list. For example:

kaons = ('K+:gen', '')
pions = ('pi+:gen', 'pionID>0.1')
fillParticleListsFromMC([kaons, pions], path=mypath)

Tip

Daughters of Lambda0 are not primary, but Lambda0 is not final state particle. Thus, when one reconstructs a particle from Lambda0, that is created with addDaughters=True and skipNonPrimaryDaughters=True, the particle always has isSignal==0. Please set options for Lambda0 to use MC-matching variables properly as follows, addDaughters=True and skipNonPrimaryDaughters=False.

Parameters:

decayString – specifies type of Particles and determines the name of the ParticleList
cut – Particles need to pass these selection criteria to be added to the ParticleList
addDaughters – adds the bottom part of the decay chain of the particle to the datastore and sets mother-daughter relations
skipNonPrimaryDaughters – if true, skip non primary daughters, useful to study final state daughter particles
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
skipNonPrimary – if true, skip non primary particle
skipInitial – if true, skip initial particles

modularAnalysis.fillSignalSideParticleList(outputListName, decayString, path)[source]#

This function should only be used in the ROE path, that is a path that is executed for each ROE object in the DataStore.

Example: fillSignalSideParticleList(‘gamma:sig’,’B0 -> K*0 ^gamma’, roe_path)

Function will create a ParticleList with name ‘gamma:sig’ which will be filled with the existing photon Particle, being the second daughter of the B0 candidate to which the ROE object has to be related.

Parameters:

ouputListName – name of the created ParticleList
decayString – specify Particle to be added to the ParticleList

modularAnalysis.findMCDecay(list_name, decay, writeOut=False, appendAllDaughters=False, skipNonPrimaryDaughters=True, path=None)[source]#

Finds and creates a ParticleList for all MCParticle decays matching a given DecayString. The decay string is required to describe correctly what you want. In the case of inclusive decays, you can use Grammar for custom MCMatching

The output particles has only the daughter particles written in the given decay string, if appendAllDaughters=False (default). If appendAllDaughters=True, all daughters of the matched MCParticle are appended in the order defined at the MCParticle level. For example,

findMCDecay('B0:Xee', 'B0 -> e+ e- ... ?gamma', appendAllDaughters=False, path=mypath)

The output ParticleList B0:Xee will match the inclusive B0 -> e+ e- decays (but neutrinos are not included), in both cases of appendAllDaughters is false and true. If the appendAllDaughters=False as above example, the B0:Xee has only two electrons as daughters. While, if appendAllDaughters=True, all daughters of the matched MCParticles are appended. When the truth decay mode of the MCParticle is B0 -> [K*0 -> K+ pi-] [J/psi -> e+ e-], the first daughter of B0:Xee is K*0 and e+ will be the first daughter of second daughter of B0:Xee.

The option skipNonPrimaryDaughters only has an effect if appendAllDaughters=True. If skipNonPrimaryDaughters=True, all primary daughters are appended but the secondary particles are not.

Tip

Daughters of Lambda0 are not primary, but Lambda0 is not a final state particle. In order for the MCMatching to work properly, the daughters of Lambda0 are appended to Lambda0 regardless of the value of the option skipNonPrimaryDaughters.

Parameters:

list_name – The output particle list name
decay – The decay string which you want
writeOut – Whether RootOutput module should save the created outputList
skipNonPrimaryDaughters – if true, skip non primary daughters, useful to study final state daughter particles
appendAllDaughters – if true, not only the daughters described in the decay string but all daughters are appended
path – modules are added to this path

modularAnalysis.getAnalysisGlobaltag(timeout=180) → str[source]#

Returns a string containing the name of the latest and recommended analysis globaltag.

Parameters:: timeout – Seconds to wait for b2conditionsdb-recommend

modularAnalysis.getAnalysisGlobaltagB2BII() → str[source]#: Get recommended global tag for B2BII analysis.

modularAnalysis.getBeamBackgroundProbability(particleList, weight, path=None)[source]#

Assign a probability to each ECL cluster as being signal like (1) compared to beam background like (0)

Parameters:

particleList – the input ParticleList, must be a photon list
weight – type of weight file to use
path – modules are added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.getFakePhotonProbability(particleList, weight, path=None)[source]#

Assign a probability to each ECL cluster as being signal like (1) compared to fake photon like (0)

Parameters:

particleList – the input ParticleList, must be a photon list
weight – type of weight file to use
path – modules are added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.getNbarIDMVA(particleList: str, path=None)[source]#

This function can give a score to predict if it is a anti-n0. It is not used to predict n0. Currently, this can be used only for ECL cluster. output will be stored in extraInfo(nbarID); -1 means MVA invalid

Parameters:

particleList – The input ParticleList name or a decay string which contains a full mother particle list name. Only one selected daughter is supported.
path – modules are added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.getNeutralHadronGeomMatches(particleLists, addKL=True, addNeutrons=False, efficiencyCorrectionKl=0.83, efficiencyCorrectionNeutrons=1.0, path=None)[source]#

For an ECL-based list, assign the mcdistanceKL and mcdistanceNeutron variables that correspond to the distance to the closest MC KL and neutron, respectively.

Parameters:

particleLists – the input ParticleLists, must be ECL-based lists (e.g. photons)
addKL – (default True) add distance to MC KL
addNeutrons – (default False) add distance to MC neutrons
efficiencyCorrectionKl – (default 0.83) apply overall efficiency correction
efficiencyCorrectionNeutrons – (default 1.0) apply overall efficiency correction
path – modules are added to this path

modularAnalysis.inclusiveBtagReconstruction(upsilon_list_name, bsig_list_name, btag_list_name, input_lists_names, path)[source]#

Reconstructs Btag from particles in given ParticleLists which do not share any final state particles (mdstSource) with Bsig.

Parameters:

upsilon_list_name – Name of the ParticleList to be filled with ‘Upsilon(4S) -> B:sig anti-B:tag’
bsig_list_name – Name of the Bsig ParticleList
btag_list_name – Name of the Bsig ParticleList
input_lists_names – List of names of the ParticleLists which are used to reconstruct Btag from

modularAnalysis.inputMdst(filename, path, environmentType='default', skipNEvents=0, entrySequence=None, *, parentLevel=0, **kwargs)[source]#

Loads the specified mDST (or uDST) file with the RootInput module.

The correct environment (e.g. magnetic field settings) is determined from environmentType. Options are either: ‘default’ (for Belle II MC and data: falls back to database), ‘Belle’: for analysis of converted Belle 1 data and MC.

Parameters:

filename (str) – the name of the file to be loaded
path (basf2.Path) – modules are added to this path
environmentType (str) – type of the environment to be loaded (either ‘default’ or ‘Belle’)
skipNEvents (int) – N events of the input file are skipped
entrySequence (str) – The number sequences (e.g. 23:42,101) defining the entries which are processed.
parentLevel (int) – Number of generations of parent files (files used as input when creating a file) to be read

modularAnalysis.inputMdstList(filelist, path, environmentType='default', skipNEvents=0, entrySequences=None, *, parentLevel=0, useB2BIIDBCache=True)[source]#

Loads the specified list of mDST (or uDST) files with the RootInput module.

The correct environment (e.g. magnetic field settings) is determined from environmentType. Options are either: ‘default’ (for Belle II MC and data: falls back to database), ‘Belle’: for analysis of converted Belle 1 data and MC.

Parameters:

filelist (list(str)) – the filename list of files to be loaded
path (basf2.Path) – modules are added to this path
environmentType (str) – type of the environment to be loaded (either ‘default’ or ‘Belle’)
skipNEvents (int) – N events of the input files are skipped
entrySequences (list(str)) – The number sequences (e.g. 23:42,101) defining the entries which are processed for each inputFileName.
parentLevel (int) – Number of generations of parent files (files used as input when creating a file) to be read
useB2BIIDBCache (bool) – Loading of local KEKCC database (only to be deactivated in very special cases)

modularAnalysis.keepInROEMasks(list_name, mask_names, cut_string, path=None)[source]#

This function is used to apply particle list specific cuts on one or more ROE masks (track or eclCluster). With this function one can KEEP the tracks/eclclusters used in particles from provided particle list. This function should be executed only in the for_each roe path for the current ROE object.

To avoid unnecessary computation, the input particle list should only contain particles from ROE (use cut ‘isInRestOfEvent == 1’). To update the ECLCluster masks, the input particle list should be a photon particle list (e.g. ‘gamma:someLabel’). To update the Track masks, the input particle list should be a charged pion particle list (e.g. ‘pi+:someLabel’).

Updating a non-existing mask will create a new one.

keep only those tracks that were used in provided particle list

keepInROEMasks('pi+:goodTracks', 'mask', '', path=mypath)

keep only those clusters that were used in provided particle list and pass a cut, apply to several masks

keepInROEMasks('gamma:goodClusters', ['mask1', 'mask2'], 'E > 0.1', path=mypath)

Parameters:

list_name – name of the input ParticleList
mask_names – array of ROEMasks to be updated
cut_string – decay string with which the mask will be updated
path – modules are added to this path

modularAnalysis.labelTauPairMC(printDecayInfo=False, path=None, TauolaBelle=False, mapping_minus=None, mapping_plus=None)[source]#

Search tau leptons into the MC information of the event. If confirms it’s a generated tau pair decay, labels the decay generated of the positive and negative leptons using the ID of KKMC tau decay table.

Parameters:

printDecayInfo – If true, prints ID and prong of each tau lepton in the event.
path – module is added to this path
TauolaBelle – if False, TauDecayMode is set. If True, TauDecayMarker is set.
mapping_minus – if None, the map is the default one, else the path for the map is given by the user for tau-
mapping_plus – if None, the map is the default one, else the path for the map is given by the user for tau+

modularAnalysis.loadGearbox(path, silence_warning=False)[source]#

Loads Gearbox module to the path.

Warning

Should be used in a job with cosmic event generation only

Needed for scripts which only generate cosmic events in order to load the geometry.

Parameters:

path – modules are added to this path
silence_warning – stops a verbose warning message if you know you want to use this function

modularAnalysis.looseMCTruth(list_name, path)[source]#

Performs loose MC matching for all particles in the specified ParticleList. The difference between loose and normal mc matching algorithm is that the loose algorithm will find the common mother of the majority of daughter particles while the normal algorithm finds the common mother of all daughters. The results of loose mc matching algorithm are stored to the following extraInfo items:

looseMCMotherPDG: PDG code of most common mother
looseMCMotherIndex: 1-based StoreArray<MCParticle> index of most common mother
looseMCWrongDaughterN: number of daughters that don’t originate from the most common mother
looseMCWrongDaughterPDG: PDG code of the daughter that doesn’t originate from the most common mother (only if looseMCWrongDaughterN = 1)
looseMCWrongDaughterBiB: 1 if the wrong daughter is Beam Induced Background Particle

Parameters:

list_name – name of the input ParticleList
path – modules are added to this path

modularAnalysis.lowEnergyPi0Identification(pi0List, gammaList, payloadNameSuffix, path=None)[source]#

Calculate low-energy pi0 identification. The result is stored as ExtraInfo lowEnergyPi0Identification for the list pi0List.

Parameters:

pi0List (str) – Pi0 list.
gammaList (str) – Gamma list. First, an energy cut E > 0.2 is applied to the photons from this list. Then, all possible combinations with a pi0 daughter photon are formed except the one corresponding to the reconstructed pi0. The maximum low-energy pi0 veto value is calculated for such photon pairs and used as one of the input variables for the identification classifier.
payloadNameSuffix (str) –
Payload name suffix. The weight payloads are stored in the analysis global tag and have the following names:
- 'LowEnergyPi0Veto' + payloadNameSuffix
- 'LowEnergyPi0Identification' + payloadNameSuffix
The possible suffixes are:
- 'Belle1' for Belle data.
- 'Belle2Release5' for Belle II release 5 data (MC14, proc12, buckets 16 - 25).
- 'Belle2Release6' for Belle II release 6 data (MC15, proc13, buckets 26 - 36).
path (basf2.Path) – Module path.

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.markDuplicate(particleList, prioritiseV0, path)[source]#

Call DuplicateVertexMarker to find duplicate particles in a list and flag the ones that should be kept

Parameters:

particleList – input particle list
prioritiseV0 – if true, give V0s a higher priority

modularAnalysis.matchMCTruth(list_name, path)[source]#

Performs MC matching (sets relation Particle->MCParticle) for all particles (and its (grand)^N-daughter particles) in the specified ParticleList.

Parameters:

list_name – name of the input ParticleList
path – modules are added to this path

modularAnalysis.mergeListsWithBestDuplicate(outputListName, inputListNames, variable, preferLowest=True, writeOut=False, ignoreMotherFlavor=False, path=None)[source]#

Merge input ParticleLists into one output ParticleList. Only the best among duplicates is kept. The lowest or highest value (configurable via preferLowest) of the provided variable determines which duplicate is the best.

Parameters:

ouputListName – name of merged ParticleList
inputListName – vector of original ParticleLists to be merged
variable – variable to determine best duplicate
preferLowest – whether lowest or highest value of variable should be preferred
writeOut – whether RootOutput module should save the created ParticleList
ignoreMotherFlavor – whether the flavor of the mother particle is ignored when trying to find duplicates
path – modules are added to this path

modularAnalysis.oldwritePi0EtaVeto(particleList, decayString, workingDirectory='.', pi0vetoname='Pi0_Prob', etavetoname='Eta_Prob', downloadFlag=True, selection='', path=None)[source]#

Give pi0/eta probability for hard photon.

In the default weight files a value of 1.4 GeV is set as the lower limit for the hard photon energy in the CMS frame.

The current default weight files are optimised using MC9. The input variables are as below. Aliases are set to some variables during training.

M: pi0/eta candidates Invariant mass
lowE: soft photon energy in lab frame
cTheta: soft photon ECL cluster’s polar angle
Zmva: soft photon output of MVA using Zernike moments of the cluster
minC2Hdist: soft photon distance from eclCluster to nearest point on nearest Helix at the ECL cylindrical radius

If you don’t have weight files in your workingDirectory, these files are downloaded from database to your workingDirectory automatically. Please refer to analysis/examples/tutorials/B2A306-B02RhoGamma-withPi0EtaVeto.py about how to use this function.

Note

Please don’t use following ParticleList names elsewhere:

gamma:HARDPHOTON, pi0:PI0VETO, eta:ETAVETO, gamma:PI0SOFT + str(PI0ETAVETO_COUNTER), gamma:ETASOFT + str(PI0ETAVETO_COUNTER)

Please don’t use lowE, cTheta, Zmva, minC2Hdist as alias elsewhere.

Parameters:

particleList – The input ParticleList
decayString – specify Particle to be added to the ParticleList
workingDirectory – The weight file directory
downloadFlag – whether download default weight files or not
pi0vetoname – extraInfo name of pi0 probability
etavetoname – extraInfo name of eta probability
selection – Selection criteria that Particle needs meet in order for for_each ROE path to continue
path – modules are added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.optimizeROEWithV0(list_name, mask_names, cut_string, path=None)[source]#

This function is used to apply particle list specific cuts on one or more ROE masks for Tracks. It is possible to optimize the ROE selection by treating tracks from V0’s separately, meaning, taking V0’s 4-momentum into account instead of 4-momenta of tracks. A cut for only specific V0’s passing it can be applied.

The input particle list should be a V0 particle list: K_S0 (‘K_S0:someLabel’, ‘’), Lambda (‘Lambda:someLabel’, ‘’) or converted photons (‘gamma:someLabel’).

Updating a non-existing mask will create a new one.

treat tracks from K_S0 inside mass window separately, replace track momenta with K_S0 momentum

optimizeROEWithV0('K_S0:opt', 'mask', '0.450 < M < 0.550', path=mypath)

Parameters:

list_name – name of the input ParticleList
mask_names – array of ROEMasks to be updated
cut_string – decay string with which the mask will be updated
path – modules are added to this path

modularAnalysis.outputIndex(filename, path, includeArrays=None, keepParents=False, mc=True)[source]#

Write out all particle lists as an index file to be reprocessed using parentLevel flag. Additional branches necessary for file to be read are automatically included. Additional Store Arrays and Relations to be stored can be specified via includeArrays list argument.

Parameters:

str – filename the name of the output index file
str – path modules are added to this path
list(str) – includeArrays: datastore arrays/objects to write to the output file in addition to particle lists and related information
bool – keepParents whether the parents of the input event will be saved as the parents of the same event in the output index file. Useful if you are only adding more information to another index file
bool – mc whether the input data is MC or not

modularAnalysis.outputMdst(filename, path)[source]#: Saves mDST (mini-Data Summary Tables) to the output root file.

Warning

This function is kept for backward-compatibility. Better to use mdst.add_mdst_output directly.

modularAnalysis.outputUdst(filename, particleLists=None, includeArrays=None, path=None, dataDescription=None)[source]#: Save uDST (user-defined Data Summary Tables) = MDST + Particles + ParticleLists The charge-conjugate lists of those given in particleLists are also stored. Additional Store Arrays and Relations to be stored can be specified via includeArrays list argument.

Note

This does not reduce the amount of Particle objects saved, see udst.add_skimmed_udst_output for a function that does.

modularAnalysis.printDataStore(eventNumber=-1, path=None)[source]#

Prints the contents of DataStore in the first event (or a specific event number or all events). Will list all objects and arrays (including size).

See also

The command line tool: b2file-size.

Parameters:

eventNumber (int) – Print the datastore only for this event. The default (-1) prints only the first event, 0 means print for all events (can produce large output)
path (basf2.Path) – the PrintCollections module is added to this path

Warning

This will print a lot of output if you print it for all events and process many events.

modularAnalysis.printList(list_name, full, path)[source]#

Prints the size and executes Particle->print() (if full=True) method for all Particles in given ParticleList. For debugging purposes.

Parameters:

list_name – input ParticleList name
full – execute Particle->print() method for all Particles
path – modules are added to this path

modularAnalysis.printMCParticles(onlyPrimaries=False, maxLevel=-1, path=None, *, showProperties=False, showMomenta=False, showVertices=False, showStatus=False, suppressPrint=False)[source]#

Prints all MCParticles or just primary MCParticles up to specified level. -1 means no limit.

By default this will print a tree of just the particle names and their pdg codes in the event, for example

[INFO] Content of MCParticle list
├── e- (11)
├── e+ (-11)
╰── Upsilon(4S) (300553)
    ├── B+ (521)
    │   ├── anti-D_0*0 (-10421)
    │   │   ├── D- (-411)
    │   │   │   ├── K*- (-323)
    │   │   │   │   ├── anti-K0 (-311)
    │   │   │   │   │   ╰── K_S0 (310)
    │   │   │   │   │       ├── pi+ (211)
    │   │   │   │   │       │   ╰╶╶ p+ (2212)
    │   │   │   │   │       ╰── pi- (-211)
    │   │   │   │   │           ├╶╶ e- (11)
    │   │   │   │   │           ├╶╶ n0 (2112)
    │   │   │   │   │           ├╶╶ n0 (2112)
    │   │   │   │   │           ╰╶╶ n0 (2112)
    │   │   │   │   ╰── pi- (-211)
    │   │   │   │       ├╶╶ anti-nu_mu (-14)
    │   │   │   │       ╰╶╶ mu- (13)
    │   │   │   │           ├╶╶ nu_mu (14)
    │   │   │   │           ├╶╶ anti-nu_e (-12)
    │   │   │   │           ╰╶╶ e- (11)
    │   │   │   ╰── K_S0 (310)
    │   │   │       ├── pi0 (111)
    │   │   │       │   ├── gamma (22)
    │   │   │       │   ╰── gamma (22)
    │   │   │       ╰── pi0 (111)
    │   │   │           ├── gamma (22)
    │   │   │           ╰── gamma (22)
    │   │   ╰── pi+ (211)
    │   ├── mu+ (-13)
    │   │   ├╶╶ anti-nu_mu (-14)
    │   │   ├╶╶ nu_e (12)
    │   │   ╰╶╶ e+ (-11)
    │   ├── nu_mu (14)
    │   ╰── gamma (22)
    ...

There’s a distinction between primary and secondary particles. Primary particles are the ones created by the physics generator while secondary particles are ones generated by the simulation of the detector interaction.

Secondaries are indicated with a dashed line leading to the particle name and if the output is to the terminal they will be printed in red. If onlyPrimaries is True they will not be included in the tree.

On demand, extra information on all the particles can be displayed by enabling any of the showProperties, showMomenta, showVertices and showStatus flags. Enabling all of them will look like this:

...
╰── pi- (-211)
    │ mass=0.14 energy=0.445 charge=-1 lifetime=6.36
    │ p=(0.257, -0.335, 0.0238) |p|=0.423
    │ production vertex=(0.113, -0.0531, 0.0156), time=0.00589
    │ status flags=PrimaryParticle, StableInGenerator, StoppedInDetector
    │ list index=48
    │
    ╰╶╶ n0 (2112)
        mass=0.94 energy=0.94 charge=0 lifetime=5.28e+03
        p=(-0.000238, -0.0127, 0.0116) |p|=0.0172
        production vertex=(144, 21.9, -1.29), time=39
        status flags=StoppedInDetector
        creation process=HadronInelastic
        list index=66

The first line of extra information is enabled by showProperties, the second line by showMomenta, the third line by showVertices and the last two lines by showStatus. Note that all values are given in Belle II standard units, that is GeV, centimeter and nanoseconds.

The depth of the tree can be limited with the maxLevel argument: If it’s bigger than zero it will limit the tree to the given number of generations. A visual indicator will be added after each particle which would have additional daughters that are skipped due to this limit. An example event with maxLevel=3 is given below. In this case only the tau neutrino and the pion don’t have additional daughters.

[INFO] Content of MCParticle list
├── e- (11)
├── e+ (-11)
╰── Upsilon(4S) (300553)
    ├── B+ (521)
    │   ├── anti-D*0 (-423) → …
    │   ├── tau+ (-15) → …
    │   ╰── nu_tau (16)
    ╰── B- (-521)
        ├── D*0 (423) → …
        ├── K*- (-323) → …
        ├── K*+ (323) → …
        ╰── pi- (-211)

The same information will be stored in the branch __MCDecayString__ of TTree created by VariablesToNtuple or VariablesToEventBasedTree module. This branch is automatically created when PrintMCParticles modules is called. Printing the information on the log message can be suppressed if suppressPrint is True, while the branch __MCDecayString__. This option helps to reduce the size of the log message.

Parameters:

onlyPrimaries (bool) – If True show only primary particles, that is particles coming from the generator and not created by the simulation.
maxLevel (int) – If 0 or less print the whole tree, otherwise stop after n generations
showProperties (bool) – If True show mass, energy and charge of the particles
showMomenta (bool) – if True show the momenta of the particles
showVertices (bool) – if True show production vertex and production time of all particles
showStatus (bool) – if True show some status information on the particles. For secondary particles this includes creation process.
suppressPrint (bool) – if True printing the information on the log message is suppressed. Even if True, the branch __MCDecayString__ is created.

modularAnalysis.printPrimaryMCParticles(path, **kwargs)[source]#

Prints all primary MCParticles, that is particles from the physics generator and not particles created by the simulation

This is equivalent to printMCParticles(onlyPrimaries=True, path=path) and additional keyword arguments are just forwarded to that function

modularAnalysis.printROEInfo(mask_names=None, full_print=False, unpackComposites=True, path=None)[source]#

This function prints out the information for the current ROE, so it should only be used in the for_each path. It prints out basic ROE object info.

If mask names are provided, specific information for those masks will be printed out.

It is also possible to print out all particles in a given mask if the ‘full_print’ is set to True.

Parameters:

mask_names – array of ROEMask names for printing out info
unpackComposites – if true, replace composite particles by their daughters
full_print – print out particles in mask
path – modules are added to this path

modularAnalysis.printVariableValues(list_name, var_names, path)[source]#

Prints out values of specified variables of all Particles included in given ParticleList. For debugging purposes.

Parameters:

list_name – input ParticleList name
var_names – vector of variable names to be printed
path – modules are added to this path

modularAnalysis.rankByHighest(particleList, variable, numBest=0, outputVariable='', allowMultiRank=False, cut='', overwriteRank=False, path=None)[source]#

Ranks particles in the input list by the given variable (highest to lowest), and stores an integer rank for each Particle in an extraInfo field ${variable}_rank starting at 1 (best). The list is also sorted from best to worst candidate (each charge, e.g. B+/B-, separately). This can be used to perform a best candidate selection by cutting on the corresponding rank value, or by specifying a non-zero value for ‘numBest’.

Tip

Extra-info fields can be accessed by the extraInfo metavariable. These variable names can become clunky, so it’s probably a good idea to set an alias. For example if you rank your B candidates by momentum,

rankByHighest("B0:myCandidates", "p", path=mypath)
vm.addAlias("momentumRank", "extraInfo(p_rank)")

Parameters:

particleList – The input ParticleList
variable – Variable to order Particles by.
numBest – If not zero, only the $numBest Particles in particleList with rank <= numBest are kept.
outputVariable – Name for the variable that will be created which contains the rank, Default is ‘${variable}_rank’.
allowMultiRank – If true, candidates with the same value will get the same rank.
cut – Only candidates passing the cut will be ranked. The others will have rank -1
overwriteRank – If true, the extraInfo of rank is overwritten when the particle has already the extraInfo.
path – modules are added to this path

modularAnalysis.rankByLowest(particleList, variable, numBest=0, outputVariable='', allowMultiRank=False, cut='', overwriteRank=False, path=None)[source]#

Ranks particles in the input list by the given variable (lowest to highest), and stores an integer rank for each Particle in an extraInfo field ${variable}_rank starting at 1 (best). The list is also sorted from best to worst candidate (each charge, e.g. B+/B-, separately). This can be used to perform a best candidate selection by cutting on the corresponding rank value, or by specifying a non-zero value for ‘numBest’.

Tip

Extra-info fields can be accessed by the extraInfo metavariable. These variable names can become clunky, so it’s probably a good idea to set an alias. For example if you rank your B candidates by dM,

rankByLowest("B0:myCandidates", "dM", path=mypath)
vm.addAlias("massDifferenceRank", "extraInfo(dM_rank)")

Parameters:

particleList – The input ParticleList
variable – Variable to order Particles by.
numBest – If not zero, only the $numBest Particles in particleList with rank <= numBest are kept.
outputVariable – Name for the variable that will be created which contains the rank, Default is ‘${variable}_rank’.
allowMultiRank – If true, candidates with the same value will get the same rank.
cut – Only candidates passing the cut will be ranked. The others will have rank -1
overwriteRank – If true, the extraInfo of rank is overwritten when the particle has already the extraInfo.
path – modules are added to this path

modularAnalysis.reconstructDecay(decayString, cut, dmID=0, writeOut=False, path=None, candidate_limit=None, ignoreIfTooManyCandidates=True, chargeConjugation=True, allowChargeViolation=False)[source]#

Creates new Particles by making combinations of existing Particles - it reconstructs unstable particles via their specified decay mode, e.g. in form of a DecayString: D0 -> K- pi+ or B+ -> anti-D0 pi+, … All possible combinations are created (particles are used only once per candidate) and combinations that pass the specified selection criteria are saved to a newly created (mother) ParticleList. By default the charge conjugated decay is reconstructed as well (meaning that the charge conjugated mother list is created as well) but this can be deactivated.

One can use an @-sign to mark a particle as unspecified for inclusive analyses, e.g. in a DecayString: '@Xsd -> K+ pi-'.

See also

Marker of unspecified particle

Warning

The input ParticleLists are typically ordered according to the upstream reconstruction algorithm. Therefore, if you combine two or more identical particles in the decay chain you should not expect to see the same distribution for the daughter kinematics as they may be sorted by geometry, momentum etc.

For example, in the decay D0 -> pi0 pi0 the momentum distributions of the two pi0 s are not identical. This can be solved by manually randomising the lists before combining.

See also

Parameters:

decayString – DecayString specifying what kind of the decay should be reconstructed (from the DecayString the mother and daughter ParticleLists are determined)
cut – created (mother) Particles are added to the mother ParticleList if they pass give cuts (in VariableManager style) and rejected otherwise
dmID – user specified decay mode identifier
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
candidate_limit – Maximum amount of candidates to be reconstructed. If the number of candidates is exceeded a Warning will be printed. By default, all these candidates will be removed and event will be ignored. This behaviour can be changed by 'ignoreIfTooManyCandidates' flag. If no value is given the amount is limited to a sensible default. A value <=0 will disable this limit and can cause huge memory amounts so be careful.
ignoreIfTooManyCandidates – whether event should be ignored or not if number of reconstructed candidates reaches limit. If event is ignored, no candidates are reconstructed, otherwise, number of candidates in candidate_limit is reconstructed.
chargeConjugation – boolean to decide whether charge conjugated mode should be reconstructed as well (on by default)
allowChargeViolation – whether the decay string needs to conserve the electric charge

modularAnalysis.reconstructDecayWithNeutralHadron(decayString, cut, allowGamma=False, allowAnyParticleSource=False, path=None, **kwargs)[source]#

Reconstructs decay with a long-lived neutral hadron e.g. $B^0 \to J/\psi K_L^0$, $B^0 \to p \bar{n} D^*(2010)^-$.

The calculation is done with IP constraint and mother mass constraint.

The decay string passed in must satisfy the following rules:

The neutral hadron must be selected in the decay string with the caret (^) e.g. B0:sig -> J/psi:sig ^K_L0:sig. (Note the caret next to the neutral hadron.)
There can only be one neutral hadron in a decay.
The neutral hadron has to be a direct daughter of its mother.

Note

This function forwards its arguments to reconstructDecay, so please check the documentation of reconstructDecay for all possible arguments.

Parameters:

decayString – A decay string following the mentioned rules
cut – Cut to apply to the particle list
allowGamma – Whether allow the selected particle to be gamma
allowAnyParticleSource – Whether allow the selected particle to be from any source. Should only be used when studying control sample.
path – The path to put in the module

modularAnalysis.reconstructMCDecay(decayString, cut, dmID=0, writeOut=False, path=None, chargeConjugation=True)[source]#

Finds and creates a ParticleList from given decay string. ParticleList of daughters with sub-decay is created.

Only the particles made from MCParticle, which can be loaded by fillParticleListFromMC, are accepted as daughters.

Only signal particle, which means isSignal is equal to 1, is stored. One can use the decay string grammar to change the behavior of isSignal. One can find detailed information in DecayString.

Tip

If one uses same sub-decay twice, same particles are registered to a ParticleList. For example, K_S0:pi0pi0 =direct=> [pi0:gg =direct=> gamma:MC gamma:MC] [pi0:gg =direct=> gamma:MC gamma:MC]. One can skip the second sub-decay, K_S0:pi0pi0 =direct=> [pi0:gg =direct=> gamma:MC gamma:MC] pi0:gg.

Tip

It is recommended to use only primary particles as daughter particles unless you want to explicitly study the secondary particles. The behavior of MC-matching for secondary particles from a stable particle decay is not guaranteed. Please consider to use fillParticleListFromMC with skipNonPrimary=True to load daughter particles. Moreover, it is recommended to load K_S0 and Lambda0 directly from MCParticle by fillParticleListFromMC rather than reconstructing from two pions or a proton-pion pair, because their direct daughters can be the secondary particle.

Parameters:

decayString – DecayString specifying what kind of the decay should be reconstructed (from the DecayString the mother and daughter ParticleLists are determined)
cut – created (mother) Particles are added to the mother ParticleList if they pass given cuts (in VariableManager style) and rejected otherwise isSignal==1 is always required by default.
dmID – user specified decay mode identifier
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
chargeConjugation – boolean to decide whether charge conjugated mode should be reconstructed as well (on by default)

modularAnalysis.reconstructMissingKlongDecayExpert(decayString, cut, dmID=0, writeOut=False, path=None, recoList='_reco')[source]#

Creates a list of K_L0’s and of B -> K_L0 + X, with X being a fully-reconstructed state. The K_L0 momentum is determined from kinematic constraints of the two-body B decay into K_L0 and X

Parameters:

decayString – DecayString specifying what kind of the decay should be reconstructed (from the DecayString the mother and daughter ParticleLists are determined)
cut – Particles are added to the K_L0 and B ParticleList if the B candidates pass the given cuts (in VariableManager style) and rejected otherwise
dmID – user specified decay mode identifier
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
recoList – suffix appended to original K_L0 and B ParticleList that identify the newly created K_L0 and B lists

modularAnalysis.reconstructRecoil(decayString, cut, dmID=0, writeOut=False, path=None, candidate_limit=None, allowChargeViolation=False)[source]#

Creates new Particles that recoil against the input particles.

For example the decay string M -> D1 D2 D3 will:

create mother Particle M for each unique combination of D1, D2, D3 Particles
Particles D1, D2, D3 will be appended as daughters to M
the 4-momentum of the mother Particle M is given by
p(M) = p(HER) + p(LER) - Sum_i p(Di)

Parameters:

decayString – DecayString specifying what kind of the decay should be reconstructed (from the DecayString the mother and daughter ParticleLists are determined)
cut – created (mother) Particles are added to the mother ParticleList if they pass give cuts (in VariableManager style) and rejected otherwise
dmID – user specified decay mode identifier
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
candidate_limit – Maximum amount of candidates to be reconstructed. If the number of candidates is exceeded no candidate will be reconstructed for that event and a Warning will be printed. If no value is given the amount is limited to a sensible default. A value <=0 will disable this limit and can cause huge memory amounts so be careful.
allowChargeViolation – whether the decay string needs to conserve the electric charge

modularAnalysis.reconstructRecoilDaughter(decayString, cut, dmID=0, writeOut=False, path=None, candidate_limit=None, allowChargeViolation=False)[source]#

Creates new Particles that are daughters of the particle reconstructed in the recoil (always assumed to be the first daughter).

For example the decay string M -> D1 D2 D3 will:

create mother Particle M for each unique combination of D1, D2, D3 Particles
Particles D1, D2, D3 will be appended as daughters to M
the 4-momentum of the mother Particle M is given by
p(M) = p(D1) - Sum_i p(Di), where i>1

Parameters:

decayString – DecayString specifying what kind of the decay should be reconstructed (from the DecayString the mother and daughter ParticleLists are determined)
cut – created (mother) Particles are added to the mother ParticleList if they pass give cuts (in VariableManager style) and rejected otherwise
dmID – user specified decay mode identifier
writeOut – whether RootOutput module should save the created ParticleList
path – modules are added to this path
candidate_limit – Maximum amount of candidates to be reconstructed. If the number of candidates is exceeded no candidate will be reconstructed for that event and a Warning will be printed. If no value is given the amount is limited to a sensible default. A value <=0 will disable this limit and can cause huge memory amounts so be careful.
allowChargeViolation – whether the decay string needs to conserve the electric charge taking into account that the first daughter is actually the mother

modularAnalysis.removeExtraInfo(particleLists=None, removeEventExtraInfo=False, path=None)[source]#: Removes the ExtraInfo of the given particleLists. If specified (removeEventExtraInfo = True) also the EventExtraInfo is removed.

modularAnalysis.removeParticlesNotInLists(lists_to_keep, path)[source]#

Removes all Particles that are not in a given list of ParticleLists (or daughters of those). All relations from/to Particles, daughter indices, and other ParticleLists are fixed.

Parameters:

lists_to_keep – Keep the Particles and their daughters in these ParticleLists.
path – modules are added to this path

modularAnalysis.removeTracksForTrackingEfficiencyCalculation(inputListNames, fraction, path=None)[source]#

Randomly remove tracks from the provided particle lists to estimate the tracking efficiency. Takes care of the duplicates, if any.

Parameters:

inputListNames (list(str)) – input particle list names
fraction (float) – fraction of particles to be removed randomly
path (basf2.Path) – module is added to this path

modularAnalysis.replaceMass(replacerName, particleLists=None, pdgCode=22, path=None)[source]#

replaces the mass of the particles inside the given particleLists with the invariant mass of the particle corresponding to the given pdgCode.

Parameters:

particleLists – new ParticleList filled with copied Particles
pdgCode – PDG code for mass reference
path – modules are added to this path

modularAnalysis.scaleError(outputListName, inputListName, scaleFactors=[1.149631, 1.085547, 1.151704, 1.096434, 1.086659], scaleFactorsNoPXD=[1.149631, 1.085547, 1.151704, 1.096434, 1.086659], d0Resolution=[0.00115328, 0.00134704], z0Resolution=[0.00124327, 0.0013272], d0MomThr=0.5, z0MomThr=0.5, path=None)[source]#

This module creates a new charged particle list. The helix errors of the new particles are scaled by constant factors. Two sets of five scale factors are defined for tracks with and without a PXD hit. The scale factors are in order of (d0, phi0, omega, z0, tanlambda). For tracks with a PXD hit, in order to avoid severe underestimation of d0 and z0 errors, lower limits (best resolution) can be set in a momentum-dependent form. This module is supposed to be used only for TDCPV analysis and for low-momentum (0-3 GeV/c) tracks in BBbar events. Details will be documented in a Belle II note, BELLE2-NOTE-PH-2021-038.

Parameters:

inputListName – Name of input charged particle list to be scaled
outputListName – Name of output charged particle list with scaled error
scaleFactors – List of five constants to be multiplied to each of helix errors (for tracks with a PXD hit)
scaleFactorsNoPXD – List of five constants to be multiplied to each of helix errors (for tracks without a PXD hit)
d0Resolution – List of two parameters, (a [cm], b [cm/(GeV/c)]), defining d0 best resolution as sqrt{ a**2 + (b / (p*beta*sinTheta**1.5))**2 }
z0Resolution – List of two parameters, (a [cm], b [cm/(GeV/c)]), defining z0 best resolution as sqrt{ a**2 + (b / (p*beta*sinTheta**2.5))**2 }
d0MomThr – d0 best resolution is kept constant below this momentum
z0MomThr – z0 best resolution is kept constant below this momentum

modularAnalysis.scaleTrackMomenta(inputListNames, scale=nan, payloadName='', scalingFactorName='SF', path=None)[source]#

Scale momenta of the particles according to a scaling factor scale. This scaling factor can either be given as constant number or as the name of the payload which contains the variable scale factors. If the particle list contains composite particles, the momenta of the track-based daughters are scaled. Subsequently, the momentum of the mother particle is updated as well.

Parameters:

inputListNames (list(str)) – input particle list names
scale (float) – scaling factor (1.0 – no scaling)
payloadName (str) – name of the payload which contains the phase-space dependent scaling factors
scalingFactorName (str) – name of scaling factor variable in the payload.
path (basf2.Path) – module is added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.selectDaughters(particle_list_name, decay_string, path)[source]#

Redefine the Daughters of a particle: select from decayString

Parameters:

particle_list_name – input particle list
decay_string – for selecting the Daughters to be preserved

modularAnalysis.setAnalysisConfigParams(configParametersAndValues, path)[source]#

Sets analysis configuration parameters.

These are:

‘tupleStyle’: ‘Default’ (default) or ‘Laconic’
- defines the style of the branch name in the ntuple
‘mcMatchingVersion’: Specifies what version of mc matching algorithm is going to be used:
- ‘Belle’ - analysis of Belle MC
- ‘BelleII’ (default) - all other cases

Parameters:

configParametersAndValues – dictionary of parameters and their values of the form {param1: value, param2: value, …)
modules – are added to this path

modularAnalysis.setBeamConstrainedMomentum(particleList, decayStringTarget, decayStringDaughters, path=None)[source]#

Replace the four-momentum of the target Particle by p(beam) - p(selected daughters). The momentum of the mother Particle will not be changed.

Parameters:

particleList – mother Particlelist
decayStringTarget – DecayString specifying the target particle whose momentum will be updated
decayStringDaughters – DecayString specifying the daughter particles used to replace the momentum of the target particle by p(beam)-p(daughters)

modularAnalysis.setupEventInfo(noEvents, path)[source]#

Prepare to generate events. This function sets up the EventInfoSetter. You should call this before adding a generator from generators. The experiment and run numbers are set to 0 (run independent generic MC in phase 3). https://confluence.desy.de/display/BI/Experiment+numbering

Parameters:

noEvents (int) – number of events to be generated
path (basf2.Path) – modules are added to this path

modularAnalysis.signalRegion(particleList, cut, path=None, name='isSignalRegion', blind_data=True)[source]#

Define and blind a signal region. Per default, the defined signal region is cut out if ran on data. This function will provide a new variable ‘isSignalRegion’ as default, which is either 0 or 1 depending on the cut provided.

Example

ma.reconstructDecay("B+:sig -> D+ pi0", "Mbc>5.2", path=path)
ma.signalRegion("B+:sig",
                 "Mbc>5.27 and abs(deltaE)<0.2",
                 blind_data=True,
                 path=path)
ma.variablesToNtuples("B+:sig", ["isSignalRegion"], path=path)

Parameters:

particleList (str) – The input ParticleList
cut (str) – Cut string describing the signal region
path (basf2.Path)
name (str) – Name of the Signal region in the variable manager
blind_data (bool) – Automatically exclude signal region from data

modularAnalysis.signalSideParticleFilter(particleList, selection, roe_path, deadEndPath)[source]#

Checks if the current ROE object in the for_each roe path (argument roe_path) is related to the particle from the input ParticleList. Additional selection criteria can be applied. If ROE is not related to any of the Particles from ParticleList or the Particle doesn’t meet the selection criteria the execution of deadEndPath is started. This path, as the name suggests should be empty and its purpose is to end the execution of for_each roe path for the current ROE object.

Parameters:

particleList – The input ParticleList
selection – Selection criteria that Particle needs meet in order for for_each ROE path to continue
for_each – roe path in which this filter is executed
deadEndPath – empty path that ends execution of or_each roe path for the current ROE object.

modularAnalysis.signalSideParticleListsFilter(particleLists, selection, roe_path, deadEndPath)[source]#

Checks if the current ROE object in the for_each roe path (argument roe_path) is related to the particle from the input ParticleList. Additional selection criteria can be applied. If ROE is not related to any of the Particles from ParticleList or the Particle doesn’t meet the selection criteria the execution of deadEndPath is started. This path, as the name suggests should be empty and its purpose is to end the execution of for_each roe path for the current ROE object.

Parameters:

particleLists – The input ParticleLists
selection – Selection criteria that Particle needs meet in order for for_each ROE path to continue
for_each – roe path in which this filter is executed
deadEndPath – empty path that ends execution of or_each roe path for the current ROE object.

modularAnalysis.smearTrackMomenta(inputListNames, payloadName='', smearingFactorName='smear', path=None)[source]#

Smear the momenta of the particles according the values read from the given payload. If the particle list contains composite particles, the momenta of the track-based daughters are smeared. Subsequently, the momentum of the mother particle is updated as well.

Parameters:

inputListNames (list(str)) – input particle list names
payloadName (str) – name of the payload which contains the smearing values
smearingFactorName (str) – name of smearing factor variable in the payload.
path (basf2.Path) – module is added to this path

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.summaryOfLists(particleLists, outputFile=None, path=None)[source]#

Prints out Particle statistics at the end of the job: number of events with at least one candidate, average number of candidates per event, etc. If an output file name is provided the statistics is also dumped into a json file with that name.

Parameters:

particleLists – list of input ParticleLists
outputFile – output file name (not created by default)

modularAnalysis.tagCurlTracks(particleLists, mcTruth=False, responseCut=-1.0, selectorType='cut', ptCut=0.5, expert_train=False, expert_filename='', path=None)[source]#

Warning

The cut selector is not calibrated with Belle II data and should not be used without extensive study.

Identifies curl tracks and tags them with extraInfo(isCurl=1) for later removal. For Belle data with a B2BII analysis the available cut based selection is described in BN1079.

The module loops over all particles in a given list with a transverse momentum below the pre-selection ptCut and assigns them to bundles based on the response of the chosen selector and the required minimum response set by the responseCut. Once all particles are assigned they are ranked by 25dr^2+dz^2. All but the lowest are tagged with extraInfo(isCurl=1) to allow for later removal by cutting the list or removing these from ROE as applicable.

Parameters:

particleLists – list of particle lists to check for curls.
mcTruth – bool flag to additionally assign particles with extraInfo(isTruthCurl) and extraInfo(truthBundleSize). To calculate these particles are assigned to bundles by their genParticleIndex then ranked and tagged as normal.
responseCut – float min classifier response that considers two tracks to come from the same particle. If set to -1 a cut value optimised to maximise the accuracy on a BBbar sample is used. Note ‘cut’ selector is binary 0/1.
selectorType – string name of selector to use. The available options are ‘cut’ and ‘mva’. It is strongly recommended to used the ‘mva’ selection. The ‘cut’ selection is based on BN1079 and is only calibrated for Belle data.
ptCut – Pre-selection cut on transverse momentum. Only tracks below that are considered as curler candidates.
expert_train – flag to set training mode if selector has a training mode (mva).
expert_filename – set file name of produced training ntuple (mva).
path – module is added to this path.

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

modularAnalysis.twoBodyISRPhotonCorrector(outputListName, inputListName, massiveParticle, path=None)[source]#

Sets photon kinematics to corrected values in two body decays with an ISR photon and a massive particle. The original photon kinematics are kept in the input particleList and can be accessed using the originalParticle() metavariable on the new list.

Parameters:

ouputListName – new ParticleList filled with copied Particles
inputListName – input ParticleList with original Particles
massiveParticle – name or PDG code of massive particle participating in the two body decay with the ISR photon
path – modules are added to this path

modularAnalysis.updateKlongKinematicsExpert(particleList, writeOut=False, path=None)[source]#

Calculates and updates the kinematics of B->K_L0 + something else with same method as reconstructMissingKlongDecayExpert. This helps to revert the kinematics after the vertex fitting.

Parameters:

particleList – input ParticleList of B meson that decays to K_L0 + X
writeOut – whether RootOutput module should save the ParticleList
path – modules are added to this path

modularAnalysis.updateMassHypothesis(particleList, pdg, writeOut=False, path=None)[source]#

Module to update the mass hypothesis of a given input particle list with the chosen PDG. A new particle list is created with updated mass hypothesis. The allowed mass hypotheses for both input and output are electrons, muons, pions, kaons and protons.

Parameters:

particleList – The input particle list name
pdg – The PDG code for the new mass hypothesis, in [11, 13, 211, 321, 2212]
writeOut – Whether RootOutput module should save the new particle list
path – Modules are added to this path

modularAnalysis.updateROEMask(list_name, mask_name, trackSelection, eclClusterSelection='', klmClusterSelection='', path=None)[source]#

Update an existing ROE mask by applying additional selection cuts for tracks and/or clusters.

See function appendROEMask!

Parameters:

list_name – name of the input ParticleList
mask_name – name of the ROEMask to update
trackSelection – decay string for the track-based particles in ROE
eclClusterSelection – decay string for the ECL-based particles in ROE
klmClusterSelection – decay string for the KLM-based particles in ROE
path – modules are added to this path

modularAnalysis.updateROEMasks(list_name, mask_tuples, path)[source]#

Update existing ROE masks by applying additional selection cuts for tracks and/or clusters.

The multiple ROE masks with their own selection criteria are specified via list tuples (mask_name, trackSelection, eclClusterSelection, klmClusterSelection)

See function appendROEMasks!

Parameters:

list_name – name of the input ParticleList
mask_tuples – array of ROEMask list tuples to be appended
path – modules are added to this path

modularAnalysis.updateROEUsingV0Lists(target_particle_list, mask_names, default_cleanup=True, selection_cuts=None, apply_mass_fit=False, fitter='treefit', path=None)[source]#

This function creates V0 particle lists (photons, $K^0_S$ and $\Lambda^0$) and it uses V0 candidates to update the Rest Of Event, which is associated to the target particle list. It is possible to apply a standard or customized selection and mass fit to the V0 candidates.

Parameters:

target_particle_list – name of the input ParticleList
mask_names – array of ROE masks to be applied
default_cleanup – if True, predefined cuts will be applied on the V0 lists
selection_cuts – a single string of selection cuts or tuple of three strings (photon_cuts, K_S0_cuts, Lambda0_cuts), which will be applied to the V0 lists. These cuts will have a priority over the default ones.
apply_mass_fit – if True, a mass fit will be applied to the V0 particles
fitter – string, that represent a fitter choice: “treefit” for TreeFitter and “kfit” for KFit
path – modules are added to this path

modularAnalysis.variableToSignalSideExtraInfo(particleList, varToExtraInfo, path)[source]#

Write the value of specified variables estimated for the single particle in the input list (has to contain exactly 1 particle) as an extra info to the particle related to current ROE. Should be used only in the for_each roe path.

Parameters:

particleList (str) – The input ParticleList
varToExtraInfo (dict[str,str]) – Dictionary of Variables (key) and extraInfo names (value).
path (basf2.Path) – modules are added to this path

modularAnalysis.variablesToDaughterExtraInfo(particleList, decayString, variables, option=0, path=None)[source]#

For each daughter particle specified via decay string the selected variables (estimated for the mother particle) are saved in an extra-info field with the given name. In other words, the property of mother is saved as extra-info to specified daughter particle.

Parameters:

particleList (str) – The input ParticleList
decayString (str) – Decay string that specifies to which daughter the extra info should be appended
variables (dict[str,str]) – Dictionary of Variables (key) and extraInfo names (value).
option (int) – Option to overwrite an existing extraInfo. Choose among -1, 0, 1, 2. An existing extra info with the same name will be overwritten if the new value is lower / will never be overwritten / will be overwritten if the new value is higher / will always be overwritten (option = -1/0/1/2).
path (basf2.Path) – modules are added to this path

modularAnalysis.variablesToEventExtraInfo(particleList, variables, option=0, path=None)[source]#

For each particle in the input list the selected variables are saved in an event-extra-info field with the given name, Can be used to save MC truth information, for example, in a ntuple of reconstructed particles.

Tip

When the function is called first time not in the main path but in a sub-path e.g. roe_path, the eventExtraInfo cannot be accessed from the main path because of the shorter lifetime of the event-extra-info field. If one wants to call the function in a sub-path, one has to call the function in the main path beforehand.

Parameters:

particleList (str) – The input ParticleList
variables (dict[str,str]) – Dictionary of Variables (key) and extraInfo names (value).
option (int) – Option to overwrite an existing extraInfo. Choose among -1, 0, 1, 2. An existing extra info with the same name will be overwritten if the new value is lower / will never be overwritten / will be overwritten if the new value is higher / will always be overwritten (option = -1/0/1/2).
path (basf2.Path) – modules are added to this path

modularAnalysis.variablesToExtraInfo(particleList, variables, option=0, path=None)[source]#

For each particle in the input list the selected variables are saved in an extra-info field with the given name. Can be used when wanting to save variables before modifying them, e.g. when performing vertex fits.

Parameters:

particleList (str) – The input ParticleList
variables (dict[str,str]) – Dictionary of Variables (key) and extraInfo names (value).
option (int) – Option to overwrite an existing extraInfo. Choose among -1, 0, 1, 2. An existing extra info with the same name will be overwritten if the new value is lower / will never be overwritten / will be overwritten if the new value is higher / will always be overwritten (option = -1/0/1/2).
path (basf2.Path) – modules are added to this path

modularAnalysis.variablesToHistogram(decayString, variables, variables_2d=None, filename='ntuple.root', path=None, *, directory=None, prefixDecayString=False, filenameSuffix='', ignoreCommandLineOverride=False)[source]#

Creates and fills a flat ntuple with the specified variables from the VariableManager

Parameters:

decayString (str) – specifies type of Particles and determines the name of the ParticleList
variables (list(tuple))) – variables + binning which must be registered in the VariableManager
variables_2d (list(tuple)) – pair of variables + binning for each which must be registered in the VariableManager
filename (str) – which is used to store the variables
path (basf2.Path) – the basf2 path where the analysis is processed
directory (str) – directory inside the output file where the histograms should be saved. Useful if you want to have different histograms in the same file to separate them.
prefixDecayString (bool) – If True the decayString will be prepended to the directory name to allow for more programmatic naming of the structure in the file.
filenameSuffix (str) – suffix to be appended to the filename before .root.
ignoreCommandLineOverride (bool) – if true, ignore override of file name via command line argument -o.

Tip

The output filename can be overridden using the -o argument of basf2.

modularAnalysis.variablesToNtuple(decayString, variables, treename='variables', filename='ntuple.root', path=None, basketsize=1600, signalSideParticleList='', filenameSuffix='', useFloat=False, storeEventType=True, ignoreCommandLineOverride=False)[source]#

Creates and fills a flat ntuple with the specified variables from the VariableManager. If a decayString is provided, then there will be one entry per candidate (for particle in list of candidates). If an empty decayString is provided, there will be one entry per event (useful for trigger studies, etc).

Parameters:

decayString (str) – specifies type of Particles and determines the name of the ParticleList
variables (list(str)) – the list of variables (which must be registered in the VariableManager)
treename (str) – name of the ntuple tree
filename (str) – which is used to store the variables
path (basf2.Path) – the basf2 path where the analysis is processed
basketsize (int) – size of baskets in the output ntuple in bytes
signalSideParticleList (str) – The name of the signal-side ParticleList. Only valid if the module is called in a for_each loop over the RestOfEvent.
filenameSuffix (str) – suffix to be appended to the filename before .root.
useFloat (bool) – Use single precision (float) instead of double precision (double) for floating-point numbers.
storeEventType (bool) – if true, the branch __eventType__ is added for the MC event type information. The information is available from MC16 on.
ignoreCommandLineOverride (bool) – if true, ignore override of file name via command line argument -o.

Tip

The output filename can be overridden using the -o argument of basf2.

modularAnalysis.writePi0EtaVeto(particleList, decayString, mode='standard', selection='', path=None, suffix='', hardParticle='gamma', pi0PayloadNameOverride=None, pi0SoftPhotonCutOverride=None, etaPayloadNameOverride=None, etaSoftPhotonCutOverride=None)[source]#

Give pi0/eta probability for hard photon.

In the default weight files a value of 1.4 GeV is set as the lower limit for the hard photon energy in the CMS frame.

The current default weight files are optimised using MC12.

The input variables of the mva training are:

M: pi0/eta candidates Invariant mass
daughter(1,E): soft photon energy in lab frame
daughter(1,clusterTheta): soft photon ECL cluster’s polar angle
daughter(1,minC2TDist): soft photon distance from eclCluster to nearest point on nearest Helix at the ECL cylindrical radius
daughter(1,clusterZernikeMVA): soft photon output of MVA using Zernike moments of the cluster
daughter(1,clusterNHits): soft photon total crystal weights sum(w_i) with w_i<=1
daughter(1,clusterE9E21): soft photon ratio of energies in inner 3x3 crystals and 5x5 crystals without corners
cosHelicityAngleMomentum: pi0/eta candidates cosHelicityAngleMomentum

The following strings are available for mode:

standard: loose energy cut and no clusterNHits cut are applied to soft photon
tight: tight energy cut and no clusterNHits cut are applied to soft photon
cluster: loose energy cut and clusterNHits cut are applied to soft photon
both: tight energy cut and clusterNHits cut are applied to soft photon

The final probability of the pi0/eta veto is stored as an extraInfo. If no suffix is set it can be obtained from the variables pi0Prob/etaProb. Otherwise, it is available as ‘{Pi0, Eta}ProbOrigin’, ‘{Pi0, Eta}ProbTightEnergyThreshold’, ‘{Pi0, Eta}ProbLargeClusterSize’, or ‘{Pi0, Eta}ProbTightEnergyThresholdAndLargeClusterSize’} for the four modes described above, with the chosen suffix appended.

Note

Please don’t use following ParticleList names elsewhere:

gamma:HardPhoton, gamma:Pi0Soft + ListName + '_' + particleList.replace(':', '_'), gamma:EtaSoft + ListName + '_' + particleList.replace(':', '_'), pi0:EtaVeto + ListName, eta:EtaVeto + ListName

Parameters:

particleList – the input ParticleList
decayString – specify Particle to be added to the ParticleList
mode – choose one mode out of ‘standard’, ‘tight’, ‘cluster’ and ‘both’
selection – selection criteria that Particle needs meet in order for for_each ROE path to continue
path – modules are added to this path
suffix – optional suffix to be appended to the usual extraInfo name
hardParticle – particle name which is used to calculate the pi0/eta probability (default is gamma)
pi0PayloadNameOverride – specify the payload name of pi0 veto only if one wants to use non-default one. (default is None)
pi0SoftPhotonCutOverride – specify the soft photon selection criteria of pi0 veto only if one wants to use non-default one. (default is None)
etaPayloadNameOverride – specify the payload name of eta veto only if one wants to use non-default one. (default is None)
etaSoftPhotonCutOverride – specify the soft photon selection criteria of eta veto only if one wants to use non-default one. (default is None)

Note

This function (optionally) requires a payload stored in the analysis GlobalTag. Please append or prepend the latest one from getAnalysisGlobaltag or getAnalysisGlobaltagB2BII.

ModularAnalysis

Contents

7.2.1. ModularAnalysis#