1 Review BDT developments Migrate to version 12, AOD BDT for W , Z  AS group

2 Study channels W  : e ,  Z  : ee ,  Contents: LO signal, 1) ISR of colliding quarks or gluons 2) FSR of W/Z decay leptons Background with fake photon, other interactions (W/Z-jet, WW, ttbar) Signal diagram Di-boson W/Z-  physics Z  channel ISR FSR TGC W  channel ISR FSR TGC

3 Muon of CBNT muonboy Electron/Photon of CBNT IsEM eg_IsEM&0x7FF = 0, match to track Efficiency by matching to MCtruth dR<0.1 Muon, Electron, Photon ID Bad match mainly on ECAL edges Barrel Region Forward Region (±)(±) ~ 95%> 98%  (e ± ) ~ 85%~ 75% ()() ~ 90%~ 70% In-Efficiency of  In-Efficiency of e In-Efficiency of  photon

4  Electron identified as photon Z  ee events, vs e  events with photon matches MCtruth electron dR<01. Ratio of Electron misidentified as photon : ~10%  Jet particles identified as photons Z   events, photon matches MCtruth particles dR<0.1 Et(  )>5 GeV, 52% of  0, .. neutral mesons 41% of  , K .. charged mesons Et(  )>25 GeV, 67% of  0, .. neutral mesons 27% of  , K .. charged mesons ~6% IsEM photons are not matched to MCtruth of wrong , GEANT4 secondary etc Electron mis-identified as Photon

5 Photon isolation, compared to fake-photon Match IsEM photons to MCtruth, dR<0.1 1)N-tracks to photon in dR<0.45 cone 2) Sum Pt of tracks to photon in 0.45 Cone Real photon  Neutral mesons  Charged mesons 

6 Z  analysis using inclusive Z pythia sample Inclusive Z samples, has Z  signal and dominant Z-jet background CSC PythiaZee events with leading IsEM e + e -, IsEM photon CSC PythiaZmumu events with leading mboy  +  -, IsEM photon ISR FSR S/N ~ 3  Electron Channel : Z , Z  e + e - vs Z-jet background m(e + e - ) Et(  ) Et(  ) m(e + e -  )  Muon Channel : Z , Z   +  - vs Z-jet background S/N ~ 6 m(  +  - ) Et(  ) Et(  ) m(  +  -  )

7 W  analysis using inclusive W pythia sample Inclusive W samples, has W  signal and dominant W-jet background CSC Pythia Wenu events with ONE leading IsEM e , IsEM photon CSC Pythia Wmunu events with ONE leading mboy  , IsEM photon -- Signal -- BKGD M T (e ) Et(  ) Et(  ) M T (e  ) S/N ~ 0.5  Electron Channel : W , W  e ± vs W-jet background S/N ~ 2 M T (  ) -- Signal -- BKGD M T (  ) Et(  ) Et(  )  Muon Channel : W , W   ± vs W-jet background

8 Expected Z  events at 1 fb -1 IsEM e+,e-,  detected with Et(  )>5 GeV 5144 Z-inclusive  =1675pb, filter=85.5%  (ee)  (ee  ) #events(1fb -1 ) 5144 Z(ee)  to be recorded by Atlas 5144 Z(ee)[  ] Z(e)[e  ] Mboy  +,  -, IsEM  detected with Et(  )>5 GeV 5145 Z-inclusive  =1675pb, filter=89.4%  (  )  (  ) #events(1fb-1) 5145 Z(  )  Z(  )[  ] W(  )[  ]

9 Expected W  events at 1 fb -1 One IsEM e , and IsEM  detected with Et(  )>5 GeV 5104 Z-inclusive  =17440pb, filter=62.5%  (e)  (e  ) #events(1fb -1 ) 5104 W(e)  to be recorded by Atlas 5104 W(e)[  ] Z(e)[  ] ttbar[e  ] X WW(e)[  ] One Mboy , and IsEM  detected with Et(  )>5 GeV 5105 Z-inclusive  =17440pb, filter=68.5%  (  )  (  ) #events(1fb-1) 5105 W(  )  W(  )[  ] Z(  )[  ] ttbar[  ] X WW(  )[  ]

10 Boosted Decision Tree for Z(  )-  to Z-fake  BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

11 Boosted Decision Tree for Z(ee)-  to Z-fake  BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

12 Boosted Decision Tree for W(  )-  to W-fake  BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

13 Boosted Decision Tree for W(e )-  to W-fake  BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

14 BDT for W(e )-  to Z xfeed BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

15 BDT for W(e )-  to ttbar xfeed BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

16 BDT for W(e )-  to WW xfeed BDT of UMich 1000 trees trainning 16 Variable :  Event topology  photon isolation S/N versus BDT cut, scaled to 

17 T2 production status & new development Production:  on-going for W inclusive, (CSC has 100k)  submission trouble resolved (had version conflict) waiting for  Suggest to have official evgen submission for W/Z inclusive continuation of simulation, reconstruction at Taiwan T2. New development:  AOD to AANT, (source code from Hong)  BDT of root 5.11 MC study:  K-factor with Baur

18 BDT in root 5.11, TMVA Advanced toolkit posted in SM meeting Rectangular cut optimisation Likelihood estimator (PDE approach) Multi-dimensional likelihood estimator (PDE - range-search approach) H-Matrix (chi-squared) estimator Fisher (and Mahalanobis) discriminant Artificial Neural Network (three different implementations) Boosted/Bagged Decision Trees RuleFit Practice made for TMVA e  channel of W(e )  vs Z(e[e]) 

19 Summary At less than 30% loss S/N by BDT is better than 10 for Z , better than 5 for W  Systematic error on reconstruction, e/ , is at the 5% level Concluding on CSC11 CBNT study proceed on writing note Migrate to and NLO for W/Z-gam