def reconstruct_B(path, lepton, kstar):
# lepton
if lepton == 'e':
lep_list = ' e+:corrected e-:corrected'
lepbrem_list = ' e+:all e-:all'
elif lepton == 'mu':
lep_list = ' mu+:all mu-:all'
# K*
if kstar == 'kst0':
charge = '0'
kstlist = 'K*0:all'
elif kstar == 'kst_ks':
charge = '+'
kstlist = 'K*+:ks'
elif kstar == 'kst_pi0':
charge = '+'
kstlist = 'K*+:pi0'
Jpsi = ' J/psi' + ':' +lepton+lepton
chain = Jpsi+' -> ' + lep_list
print(purple, 'reconstruct decay:', end, cyan, chain, end)
reconstructDecay(decayString=chain,cut='dM<0.11', path=path)
# combine J/psi and Kst candidates to form B0 candidates
Bmeson = 'B'+charge+':'+kstar+'Jpsi'
chain = Bmeson+' -> ' + kstlist + Jpsi
print(purple, 'reconstruct decay:', end, cyan, chain, end)
# reconstruction
reconstructDecay(decayString=chain,
cut='Mbc > 5.2 and abs(deltaE) < 0.3', path=path)
matchMCTruth(list_name=Bmeson, path=path)
# variablesToNtuple(chain, ['M', 'p', 'E', 'useCMSFrame(p)', 'useCMSFrame(E)', 'daughter(0, kaonID)',
# 'daughter(1, pionID)', 'isSignal', 'mcErrors'],
# filename='ntuple.root', treename="D", path=path)
# ROE and vfit
continuum(Bmeson, path)
Apply_VerTexFit(path, lepton, kstar)
vertex.TagV(list_name=Bmeson, maskName='cleanMask'+Bmeson, path=path)
def reconstruct_B(path, lepton, kstar):
# lepton
if lepton == 'e':
lep_list = ' e+:corrected e-:corrected'
lepbrem_list = ' e+:all e-:all'
elif lepton == 'mu':
lep_list = ' mu+:all mu-:all'
# K*
if kstar == 'kst0':
charge = '0'
kstlist = 'K*0:all'
elif kstar == 'kst_ks':
charge = '+'
kstlist = 'K*+:ks'
elif kstar == 'kst_pi0':
charge = '+'
kstlist = 'K*+:pi0'
Bmeson = 'B'+charge+':'+kstar+lepton+lepton
chain = Bmeson+' -> ' + kstlist + lep_list
print(purple, 'reconstruct decay:', end, cyan, chain, end)
# reconstruction
reconstructDecay(decayString=chain,
cut='5.2 < Mbc < 5.29 and -0.3 < deltaE < 0.3', path=path)
matchMCTruth(list_name=Bmeson, path=path)
# ROE and vfit
continuum(Bmeson, path)
Apply_VerTexFit(path, lepton, kstar)
vertex.TagV(list_name=Bmeson, maskName='cleanMask'+Bmeson, path=path)
if lepton == 'e':
Bmeson = 'B'+charge+':'+kstar+lepton+lepton + '_brem'
chain = Bmeson+' -> ' + kstlist + lep_list + lepbrem_list
print(purple, 'reconstruct decay:', end, cyan, chain, end)
reconstructDecay(decayString=chain,
cut='5.2 < Mbc < 5.3 and -0.3 < deltaE < 0.3', path=path)
path=main)
# match reconstructed with MC particles
ma.matchMCTruth('B0', path=main)
# build the rest of the event
ma.buildRestOfEvent('B0', fillWithMostLikely=True, path=main)
# call flavor tagging
ft.flavorTagger('B0', path=main)
# remove B0 candidates without a valid flavor information
ma.applyCuts('B0', 'qrOutput(FBDT) > -2', path=main)
# fit B vertex on the tag-side
vertex.TagV('B0', constraintType='tube', fitAlgorithm='Rave', path=main)
# perform best candidate selection
#b2.set_random_seed('USBelleIISummerSchool')
#ma.rankByHighest('B0', variable='random', numBest=1, path=main)
# create list of variables for output ntuple
standard_vars = vc.kinematics + vc.mc_kinematics + vc.mc_truth
fs_vars = vc.pid + vc.track + vc.track_hits + standard_vars
jpsi_ks_vars = vc.inv_mass + vc.vertex + vc.mc_vertex + standard_vars
b_vars = vc.deltae_mbc + vc.tag_vertex + vc.mc_tag_vertex + ft.flavor_tagging + standard_vars
b_vars += vu.create_aliases_for_selected([*fs_vars, 'isBremsCorrected'],
'B0 -> [J/psi -> ^e+ ^e-] K_S0',
prefix=['ep', 'em'])
b_vars += vu.create_aliases_for_selected(fs_vars,
# define the "cleaner" mask
eclCut = '[E > 0.062 and abs(clusterTiming) < 18 and clusterReg==1] or \
[E>0.060 and abs(clusterTiming) < 20 and clusterReg==2] or \
[E>0.056 and abs(clusterTiming) < 44 and clusterReg==3]'
cleanMask = ('cleanMask', 'abs(d0) < 10.0 and abs(z0) < 20.0', eclCut)
# append both masks to ROE
ma.appendROEMasks('B0:signal', [cleanMask], path=main_path)
# choose one mask which is applied
ma.buildContinuumSuppression('B0:signal', 'cleanMask', path=main_path)
ma.matchMCTruth('B0:signal', path=main_path)
vtx.TagV('B0:signal', 'internal', path=main_path)
ft.flavorTagger(particleLists = 'B0:signal', weightFiles='B2nunubarBGx1', path=main_path)
#matchMCTruth('pi+:all')
#matchMCTruth(Kres+':all')
#matchMCTruth('gamma:loose')
main_path.add_module('MVAExpert', listNames=['B0:signal'], extraInfoName='CSMVA',
identifier='./mva-addition/MyTMVA.xml')
ma.writePi0EtaVeto('B0:signal', 'B0 -> pi+:good pi-:good K_S0:all ^gamma', path=main_path)
#myVetoVariables()
kin_variables = vc.mc_truth + ['p', 'E', 'pCMS', 'ECMS', 'cosTheta', 'phi',
'M', 'dM', 'chiProb','charge', 'PDG']
ft_variables = makePretty(['qrOutput(FBDT)', 'qrOutput(FANN)', 'isRelatedRestOfEventB0Flavor', 'qOutput(FANN)', 'rBinBelle(FANN)', 'qOutput(FBDT)', 'rBinBelle(FBDT)', 'EW90', 'pMissCMS', 'cosThetaMissCMS'])
def reconstruction(input_file, output_file):
my_path = b2.create_path()
ma.inputMdst("default", input_file, my_path)
# Find decay name from the input file name
decay_name = "_".join(input_file.split("/")[-1].split("_")[0:2])
# Load configuration file
config = yaml.safe_load(open("config/reco_config.yaml"))
options = config[decay_name]
# Parse list of subdecays in decay chain
decays = options["sub_decays"]
decays = DecayList(decays)
# Create particle lists for final state particles
fsps = decays.get_fsps()
for particle in fsps:
ma.fillParticleList(particle, "", path=my_path)
# Reconstruct requested decay chains
for decay in decays:
ma.reconstructDecay(str(decay), "", path=my_path)
# Perform truth matching for requested particles
truth_match_particles = decays.mothers
for truth_match_particle in truth_match_particles:
ma.looseMCTruth(truth_match_particle, path=my_path)
# Perform vertex fitting
head = decays.get_head()
vtx_decay_string = decays.get_chain()
print(vtx_decay_string)
vx.vertexRave(head, 0, vtx_decay_string, constraint="iptube", path=my_path)
# ma.rankByLowest("B0", 'chiProb', numBest=3, outputVariable='B_vtx_rank', path=my_path)
# ma.variables.addAlias('B_vtx_rank', 'extraInfo(B_vtx_rank)')
ma.buildRestOfEvent(head, path=my_path)
# Tag-side
vx.TagV(head, "breco", 0.001, path=my_path)
ma.buildEventKinematics(path=my_path)
ma.buildEventShape(path=my_path)
# Create centre-of-mass frame variables
cms_kinematics = vu.create_aliases(
vc.kinematics, "useCMSFrame({variable})", prefix="CMS"
)
variables = [
item
for sublist in [
vc.kinematics,
cms_kinematics,
vc.deltae_mbc,
vc.inv_mass,
vc.event_shape,
vc.vertex,
vc.mc_truth,
vc.mc_kinematics,
vc.mc_vertex,
vc.mc_tag_vertex,
]
for item in sublist
]
trees = yaml.safe_load(open("config/tree_names.yaml"))
for particle in decays.all_particles:
ma.variablesToNtuple(
particle,
variables,
filename=output_file,
treename=trees[particle],
path=my_path,
)
b2.process(my_path)
print(b2.statistics)
# You can apply cuts using the flavor Tagger: qrOutput(FBDT) > -2 rejects all events which do not
# provide flavor information using the tag side
ma.applyCuts(list_name='B0:jspipi0', cut='qrOutput(FBDT) > -2', path=my_path)
# If you applied the cut on qrOutput(FBDT) > -2 before then you can rank by highest r- factor
ma.rankByHighest(particleList='B0:jspipi0',
variable='abs(qrOutput(FBDT))',
numBest=0,
outputVariable='Dilution_rank',
path=my_path)
# Fit Vertex of the B0 on the tag side
vx.TagV(list_name='B0:jspipi0',
MCassociation='breco',
confidenceLevel=0.001,
useFitAlgorithm='standard_PXD',
path=my_path)
# Select variables that we want to store to ntuple
fs_vars = vc.pid + vc.track + vc.mc_truth
jpsiandk0s_vars = vc.mc_truth
bvars = vc.reco_stats + \
vc.deltae_mbc + \
vc.mc_truth + \
vc.roe_multiplicities + \
vc.flavor_tagging + \
vc.tag_vertex + \
vc.mc_tag_vertex + \
vu.create_aliases_for_selected(list_of_variables=fs_vars,
decay_string='B0 -> [J/psi -> ^mu+ ^mu-] pi0') + \