B2A602-BestCandidateSelection.py

#!/usr/bin/env python3
 
 
 
 
 
import basf2 as b2
import modularAnalysis as ma
import variables.collections as vc
import variables.utils as vu
import vertex as vx
import stdCharged as stdc
import variables as va
 
# create path
my_path = b2.create_path()
 
# load input ROOT file
ma.inputMdst(filename=b2.find_file('B2pi0D_D2hh_D2hhh_B2munu.root', 'examples', False),
             path=my_path)
 
# use standard final state particle lists
#
# creates "pi+:all" ParticleList (and c.c.)
stdc.stdPi('all', path=my_path)
# rank all pions of the event by momentum magnitude
# variable stored to extraInfo as pi_p_rank
ma.rankByLowest(particleList='pi+:all',
                variable='p',
                outputVariable='pi_p_rank',
                path=my_path)
 
va.variables.addAlias('pi_p_rank', 'extraInfo(pi_p_rank)')
 
# creates "K+:loose" ParticleList (and c.c.)
stdc.stdK(listtype='loose', path=my_path)
 
# keep only candidates with 1.8 < M(Kpi) < 1.9 GeV
ma.reconstructDecay(decayString='D0 -> K-:loose pi+:all',
                    cut='1.8 < M < 1.9',
                    path=my_path)
 
# perform MC matching (MC truth association)
ma.matchMCTruth(list_name='D0', path=my_path)
 
# perform D0 vertex fit
# keep only candidates passing C.L. value of the fit > conf_level
vx.treeFit(list_name='D0',
           conf_level=0,  # 0: keep only fit survivors, -1: keep all candidates; optimise this cut for your need
           ipConstraint=True,
           # pins the B0 PRODUCTION vertex to the IP (increases SIG and BKG rejection) use for better vertex resolution
           updateAllDaughters=True,  # update momenta of ALL particles
           path=my_path)
 
# smaller |M_rec - M| is better, add here a different output variable name, due to parentheses
ma.rankByLowest(particleList='D0',
                variable='abs(dM)',
                outputVariable='abs_dM_rank',
                path=my_path)
 
# maybe not the best idea, but might cut away candidates with failed fits
ma.rankByHighest(particleList='D0',
                 variable='chiProb',
                 path=my_path)
 
# One can store quantities of candidates with specific ranks.
# Here, we look at the PID of the pion for the D0 candidates with rank 1 and 2 and the difference of those values.
# The second argument of getVariableByRank is the ordering variable of the previous ranking.
va.variables.addAlias('D0_rank1_pi_PIDpi', 'getVariableByRank(D0, chiProb, daughter(1, pionID), 1)')
va.variables.addAlias('D0_rank2_pi_PIDpi', 'getVariableByRank(D0, chiProb, daughter(1, pionID), 2)')
 
# It makes sense to put these variables (or a combination of them) into an event-based ExtraInfo:
ma.variablesToEventExtraInfo('D0', {'formula(D0_rank1_pi_PIDpi - D0_rank2_pi_PIDpi)': 'D0_pi_PIDpi_r1_minus_r2'}, path=my_path)
va.variables.addAlias('D0_pi_PIDpi_r1_minus_r2', 'eventExtraInfo(D0_pi_PIDpi_r1_minus_r2)')
 
# Now let's do mixed ranking:
# First, we want to rank D candidates by the momentum of the pions
# Second, we want to rank those D candidates that were built with the highest-p by the vertex Chi2
# This doesn't have any sense, but shows how to work with consecutive rankings
#
# Let's add alias for the momentum rank of pions in D
va.variables.addAlias('D1_pi_p_rank', 'daughter(1,pi_p_rank)')
# Ranking D candidates by this variable.
# Candidates built with the same pion get the same rank (allowMultiRank=True).
ma.rankByHighest(particleList='D0',
                 variable='D1_pi_p_rank',
                 allowMultiRank=True,
                 outputVariable="first_D_rank",
                 path=my_path)
va.variables.addAlias('first_D_rank', 'extraInfo(first_D_rank)')
# Now let's rank by chiProb only those candidates that are built with the highest momentum pi
# Other candidates will get this rank equal to -1
ma.rankByHighest(particleList="D0",
                 variable="chiProb",
                 cut="first_D_rank == 1",
                 outputVariable="second_D_rank",
                 path=my_path)
va.variables.addAlias('second_D_rank', 'extraInfo(second_D_rank)')
 
# add rank variable aliases for easier use
va.variables.addAlias('dM_rank', 'extraInfo(abs_dM_rank)')
va.variables.addAlias('chiProb_rank', 'extraInfo(chiProb_rank)')
 
# Select variables that we want to store to ntuple
fs_hadron_vars = vu.create_aliases_for_selected(list_of_variables=vc.mc_truth, decay_string='D0 -> ^K- ^pi+')
 
d0_vars = vc.vertex + \
    vc.mc_vertex + \
    vc.mc_truth + \
    fs_hadron_vars + \
    ['dM', 'chiProb', 'dM_rank', 'chiProb_rank', 'D1_pi_p_rank', 'first_D_rank', 'second_D_rank', 'D0_pi_PIDpi_r1_minus_r2']
 
 
# Saving variables to ntuple
output_file = 'B2A602-BestCandidateSelection.root'
ma.variablesToNtuple(decayString='D0',
                     variables=d0_vars,
                     filename=output_file,
                     treename='D0',
                     path=my_path)
 
 
# Process the events
b2.process(my_path)
 
# print out the summary
print(b2.statistics)