def calc_tag_prob_Q( self, iL, btag_vals ):
'''Returns the 3 jet b-tagging probability assuming bqq and iL, the index of the jet from the leptonic b'''
assert iL >= 0 and iL <= 2
prob = U.hist_value_at( self.b_tag_hist, btag_vals[ iL ] )
for iW in range( 3 ):
if iW != iL :
prob *= U.hist_value_at( self.w_tag_hist, btag_vals[ iW ] )
return prob
def calc_tag_prob_H( self, iW, btag_vals ):
'''Returns the 3 jet b-tagging probability assuming bbq and iW, the index of the jet from the hadronic W decay'''
assert iW >= 0 and iW <= 2
prob = U.hist_value_at( self.w_tag_hist, btag_vals[ iW ] )
for iB in range( 3 ):
if iB != iW :
prob *= U.hist_value_at( self.b_tag_hist, btag_vals[ iB ] )
return prob
def reco( self, lv_lep, lv_jets, mex, mey, true_leptonic_b_index, cheat = False,
btag_vals = [], known_problem_with_lepton = False ):
'''Does a partial reconstruction of ttbar --> ( b (l nu) ) [ b q ... ].
The "cheat" option means to consider only the correct assignment (if any).
The parton_matches and known_problem_with_lepton are used to cheat and to create debug information
'''
Nj = len( lv_jets )
assert Nj == 3
assert (not cheat) or true_leptonic_b_index in range(3)
dbg = self.dbg
if dbg > 50:
print 'partial_reco inputs are reasonable.'
self.matchable = true_leptonic_b_index >= 0 and not known_problem_with_lepton
# prepare the observables (one per reco-assignments)
SR = self.SR
OK = SR.reco_nu( mex, mey, lv_lep )
if not OK:
raise Exception( 'Unexpected failure of simple_reco::reco_nu' )
if SR.two_solutions() :
lv_nu_pzs = [ SR.nu(), SR.alt_nu() ]
if dbg > 29:
print 'DBG30PR SR(',mex,',',mey,',',U.lv_as_EPM_str(lv_lep),') -> '\
'2?',self.SR.two_solutions(),'nu:',U.lv_as_3tup(SR.nu()),'/',U.lv_as_3tup(SR.alt_nu())
lv_nu_iLs = []
lv_tl_iLs = []
mtls = []
mps = []
lv_3j = sum( lv_jets, LV.new() )
for iL in range( 3 ) :
self.perm_is_correct = iL == true_leptonic_b_index
lv_LBL = lv_jets[ iL ] + lv_lep
if self.SR.two_solutions() :
leptonic_tops = [ lv_LBL + lv for lv in lv_nu_pzs ]
dmtops = [ abs( self.top_mass - lv.M() ) for lv in leptonic_tops ]
iNu = 0 if dmtops[0] < dmtops[1] else 1
if dbg > 18:
print 'DBG19PR iL:',iL,'-> perm_is_correct:',self.perm_is_correct,', dmtops:',dmtops,'-> iNu:',iNu
lv_nu_iLs.append( lv_nu_pzs[ iNu ] )
lv_tl = leptonic_tops[ iNu ]
else:
lv_nu = self.SR.nu()
lv_nu_iLs.append( lv_nu )
lv_tl = lv_LBL + lv_nu
if dbg > 14:
print 'DBG15PR iL:',iL,'-> nu:',U.lv_as_EPM_str(lv_nu_iLs[-1]),', tl:',U.lv_as_EPM_str(lv_tl)
lv_tl_iLs.append( lv_tl )
mtls.append( lv_tl.M() )
mps.append( ( lv_3j - lv_jets[ iL ] ).M() )
# loop over statistics-assignments
recos = []
for iL in range( 3 ) :
self.perm_is_correct = iL == true_leptonic_b_index
if cheat and not self.matchable and self.perm_is_correct:
continue
lv_nu = lv_nu_iLs[ iL ]
lv_tl = lv_tl_iLs[ iL ]
for iH in range( 3 ) : # loop over hadronic b assignment
if iH == iL :
continue
iW = 3 - iL - iH
iLHW = [iL,iH,iW]
# probabilities for hypothesis H: lhq (i.e. a jet from the hadronic W is missing)
prob_t_H = U.hist_value_at( self.h_mt_l, mtls[ iL ] ) * \
U.hist_value_at( self.h_mt_h, mtls[ iH ] ) * \
U.hist_value_at( self.h_mt_q, mtls[ iW ] )
prob_p_H = U.hist_value_at( self.h_mp_hq, mps[ iL ] ) * \
U.hist_value_at( self.h_mp_lq, mps[ iH ] ) * \
U.hist_value_at( self.h_mp_hl, mps[ iW ] )
prob_tag_H = self.calc_tag_prob_H( iW, btag_vals )
prob_H = prob_t_H * prob_p_H * prob_tag_H
# probabilities for hypothesis Q: lqq (i.e. the hadronic b is missing)
prob_t_Q = U.hist_value_at( self.h_mt_l, mtls[ iL ] ) * \
U.hist_value_at( self.h_mt_q, mtls[ iH ] ) * \
U.hist_value_at( self.h_mt_q, mtls[ iW ] )
prob_p_Q = U.hist_value_at( self.h_mp_qq, mps[ iL ] ) * \
U.hist_value_at( self.h_mp_lq, mps[ iH ] ) * \
U.hist_value_at( self.h_mp_lq, mps[ iW ] )
prob_tag_Q = self.calc_tag_prob_Q( iL, btag_vals )
prob_Q = prob_t_Q * prob_p_Q * prob_tag_Q
if dbg > 28:
print 'DBG29PR iLHW:',iLHW,'-> prob_tag_H:',prob_tag_H,'prob_tag_Q:',prob_tag_Q
prob = self.fQ * prob_Q + ( 1-self.fQ ) * prob_H
postriori_fQ = ( self.fQ * prob_Q / prob ) if prob > 0 else -1
lv_th_proxy = lv_jets[ iH ] + lv_jets[ iW ]
xx = R.TMath.Range( 0.2, 1.2, 100 / lv_th_proxy.E() )
alpha = 0.6500 + 0.7076 * xx
lv_ttbar = lv_tl + lv_th_proxy * alpha
recos.append( { 'iLHW' : iLHW, 'tagH' : prob_tag_H, 'tagQ' : prob_tag_Q,
'tl' : lv_tl, 'proxy' : lv_th_proxy, 'nu' : lv_nu,
'alpha' : alpha, 'ttbar' : lv_ttbar, 'cmtt' : ( lv_ttbar.M() - 64.44 ) / 0.7596,
'fQ' : postriori_fQ, 'prob' : prob } )
if dbg > 34:
print 'DBG35PR alpha:',alpha,'prob_Q:',prob_Q,'prob_H',prob_H
recos.sort( key=lambda reco: - reco[ 'prob' ] )
denom = sum( [ reco['prob'] for reco in recos ] )
if denom <= 0:
if dbg :
print 'Information (DBG1): partial reco probs are non-positive:',\
[ reco['prob'] for reco in recos ],'(legitimate as long as it is rare)'
averages = self.averagables_array( recos[0], lv_lep ) [1:]
else:
cands = [ self.averagables_array( reco, lv_lep ) for reco in recos ]
if dbg > 21 :
print 'DBG22 partial_reco\'s cands for averaging are',cands
weighted_cands = [ [cand[0]*obs for obs in cand[1:] ] for cand in cands ]
averages = [ sum(column)/denom for column in zip( *weighted_cands ) ]
if dbg > 14 :
print 'DBG15 partial_reco\'s averages:',averages
return recos, { 'mtt': averages[ 0 ], 'yl': averages[ 1 ], 'yh': averages[ 2 ],
'cb' : averages[ 3 ], 'ch': averages[ 4 ], 'ct': averages[ 5 ] }
