def create_path(self):
path = basf2.create_path()
modularAnalysis.inputMdstList(
'default',
self.get_input_file_names("reconstructed_output.root"),
path=path)
modularAnalysis.fillParticleLists([('K+', 'kaonID > 0.1'),
('pi+', 'pionID > 0.1')],
path=path)
modularAnalysis.reconstructDecay('D0 -> K- pi+',
'1.7 < M < 1.9',
path=path)
modularAnalysis.matchMCTruth('D0', path=path)
modularAnalysis.reconstructDecay('B- -> D0 pi-',
'5.2 < Mbc < 5.3',
path=path)
try: # treeFit is the new function name in light releases after release 4 (e.g. light-2002-janus)
vertex.treeFit('B+', 0.1, update_all_daughters=True, path=path)
except AttributeError: # vertexTree is the function name in release 4
vertex.vertexTree('B+', 0.1, update_all_daughters=True, path=path)
modularAnalysis.matchMCTruth('B-', path=path)
modularAnalysis.variablesToNtuple(
'D0', [
'M', 'p', 'E', 'useCMSFrame(p)', 'useCMSFrame(E)',
'daughter(0, kaonID)', 'daughter(1, pionID)', 'isSignal',
'mcErrors'
],
filename=self.get_output_file_name("D_n_tuple.root"),
path=path)
modularAnalysis.variablesToNtuple(
'B-', ['Mbc', 'deltaE', 'isSignal', 'mcErrors', 'M'],
filename=self.get_output_file_name("B_n_tuple.root"),
path=path)
return path
def create_analysis_path(
b_ntuple_filename="B_ntuple.root",
d_ntuple_filename="D_ntuple.root",
mbc_range=(5.2, 5.3),
):
"""
Example of a minimal reconstruction with a cut as a changeable function
parameter, adapted from code in the ``B2T_Basics_3_FirstAnalysis.ipynb``
notebook from b2 starter kit.
"""
path = basf2.create_path()
# this local inputMdstList will only be used when this steerig file is run locally, gbasf2 overrides it
local_input_files = [
"/group/belle2/dataprod/MC/MC13a/prod00009434/s00/e1003/4S/r00000/mixed/mdst/sub00/mdst_000001_prod00009434_task10020000001.root"
]
mA.inputMdstList(
environmentType="default",
filelist=local_input_files,
path=path,
)
stdK("higheff", path=path)
stdPi("higheff", path=path)
mA.reconstructDecay('D0:Kpi -> K-:higheff pi+:higheff',
'1.7 < M < 1.9',
path=path)
# use try except to have this code work for both the old and new function names for the tree fit
mA.matchMCTruth('D0:Kpi', path=path)
mA.reconstructDecay('B- -> D0:Kpi pi-:higheff',
f"{mbc_range[0]} < Mbc < {mbc_range[1]}",
path=path)
try:
vx.treeFit('B+', 0.1, path=path)
except AttributeError:
vx.vertexTree('B+', 0.1, path=path)
mA.setAnalysisConfigParams({"mcMatchingVersion": "BelleII"}, path)
mA.matchMCTruth('B-', path=path)
vm.addAlias("p_cms",
"useCMSFrame(p)") # include aliases to test if they work
vm.addAlias("E_cms", "useCMSFrame(E)")
mA.variablesToNtuple('D0:Kpi', [
'M', 'p', 'E', 'E_cms', 'p_cms', 'daughter(0, kaonID)',
'daughter(1, pionID)', 'isSignal', 'mcErrors'
],
filename=d_ntuple_filename,
treename="D",
path=path)
mA.variablesToNtuple('B-', ['Mbc', 'deltaE', 'isSignal', 'mcErrors', 'M'],
filename=b_ntuple_filename,
treename="B",
path=path)
return 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 D0 vertex fit
# keep candidates only passing C.L. value of the fit > 0.0 (no cut)
vx.vertexTree(
list_name='D0',
conf_level=
-1, # keep all cadidates, 0:keep only fit survivors, 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 off 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)
# Now let's do mixed ranking:
# First, we want to rank D candiadtes by the momentum of the pions