1. Status Report on  C. Di Donato, B. Di Micco, M. Jacewicz Phi-Decay WG Meeting February 9 2010

2. Outline • Analysis • Data-MC comparison • Momenta Smearing • Evaluation of systematics • Discussion with referee • New smearing method • Track efficiency correction applied on MC (as in the Memo343); • Cross-check with Kinematic fit • PCV EVCL vs tracks selected in the analysis

3. Analysis • Event Signature: • 2 Prompt Neutral Clusters: |tcl-lcl/c|<5t • Recoil photon: most energetic cluster with E250 MeV • 2 tracks closest to IP (using PCA, no vertex requirement) • Kinematical Constraints: • Two body decay kinematics to calculate E recoil •  kinematics to calculate  • : |Et-Pt|<10 MeV (EtPt) • Best Photon: we choose one PNC with <0.13 rad to the calculated (OPAN) • Background: main one is 0: M(M • in the 0 rest frame cos • Only barrel • TOF(Time Of Flight cut to reject bhabha background)-ANGLE

4. DATA-MC comparison and MC smearing Let’s go back to the smearing, starting from control sample selected to check Data-MC: • selected using reversed cut on cos • we look at missing mass from p+p-

5. Data – MC and smearing: first approach Missing mass on control sample We find discrepancy and try to solve smearing the Pt OLD APPROACH

6. After Smearing OLD APPROACH

7. After Smearing Only  background is included All background is included Residual discrepancy on the right tail. Smearing method? Background estimation? OLD APPROACH

8. Data-MC comparison qpp qpp Opening angle between  qpp

9. Data-MC comparison Mpp (MeV) Mpp (MeV)  Invariant Mass Mpp (MeV)

10. Data-MC Pp (MeV) Pp (MeV) P Pp (MeV)

11. Data-MC qp qp  qp

12. Summary on Systematics After smearing systematics for angular cuts remains practically unchanged. We look at cosand cos(OpAn) for  cluster also in the transverse variable

13. Summary on Systematics Only transverse angle xy plane Full cos

14. Summary on Systematics Cos(Full OpAn ) Only transverse angle

15. The situation is much better, systematic reduced from 1-2% to 1-2 per mil, the price we pay is the background contamination: OPAN–Cos–ANGLE–TOF–onlybarrel: Cut Eff = 25.67% Signal events = 564765, PHI Bkg = 6.7% ETA Bkg = 30% OPAN–Cos–ANGLE–TOF–onlybarrel–EtPt: Cut Eff = 24.89% Signal events = 547759, PHI Bkg = 3% ETA Bkg = 0.2% TransOPAN–CosTrans–ANGLE–TOF–onlybarrel: Cut Eff = 24.26% Signal events = 533938, PHI Bkg = 25% ETA Bkg = 120% TransOPAN–CosTrans–ANGLE–TOF–onlybarrel–EtPt: Cut Eff = 22.98% Signal events = 505692, PHI Bkg = 11% ETA Bkg = 1% OLD CUTS: NEW CUTS:

16. New Systematics Transverse cos OK

17. New Systematics Cos(Transv. OpAn ) OK

18. New Systematics Et-Pt The systematic on Et-Pt doesn't change.

19. New Systematics E-P

20. Spectra after smearing

21. Spectra before smearing

22. Unbiased sample: only preselection

23. Outline (II) • New data sample to “play with” • 560 pb-1 preselected with basic conditions: to study smearing effect we introduce less kinematic constraints, and ask only ≥2 Prompt Neutral Cluster, one with E≥250MeV • This sample was used for the recent study of systematics and new smearing

24. What’s new: Different approach for the momenta:we take into account curv from DTFS cov matrix Better fitting We use the smearing function: Full fit with 3 params: 1) Total Scale: 0.8808 2) shift: 337.8 • 10-6 3) smearing: 0.1470

25. Smearing: Old method versus New NEW: Full fit with 3 parameters: Total Scale, shift, smearing OLD: simple method each parameter Fitted separately

26. E – P cut problemEven with new smearing we do not get satisfactory comparison with MC    eeg continuum Unbiased sample: only preselection Now is clear the problem is on the background estimation

27. We try to fit the Et-Pt (E–P) spectrum to see if the discrepancy is due to a bad Signal/Background estimation Data – MC.

28. E-P: with all MC contributionssatisfying agreement

29. Output from TFractionFitter (1200bins in the histograms)   eeg continuum  2 = 1728 Ndf = 1196

30. After smearing and fit… • Data-MC satisfying agreement • Branching ratio stability and Systematic seems to be under control, we are reevaluating the systematics. Other checks: • kinematic fit • Prompt Charged Vertex from EVCL

31. Kinematic fit 21 Input Parameters: 2 PNC: 2  5 = 10 parameters 2 tracks at PCA: 2  3 = 6 parameters IP position: 3 parameters Beam info: 2 parameters 7 Constraints: Time of flight of photons: 2 4-momentum conservation: 4  invariant mass

32. RAD Stream • DATA-MC comparison • 2 distribution • Analysys cut: • RAD • OPAN • Cos • ANGLE • TOF • Onlybarrel • EtPt 

33. RPI Stream • DATA-MC comparison • 2 distribution • Analysys cut: • RAD • OPAN • Cos • ANGLE • TOF • Onlybarrel • EtPt 

34. RAD True-Rec True-Fit RAD Stream P+ (Ptrue-Pfit) (Ptrue-Prec) P- (Ptrue-Prec) (Ptrue-Pfit)

35. RPI True-Rec True-Fit RPI Stream P+ (Ptrue-Pfit) (Ptrue-Prec) P- (Ptrue-Prec) (Ptrue-Pfit)

36. PCV Check: EVCL requirement on VTX • PCV means there is the VTX as from EVCL requirement • Match means the tracks we choose are the one connected to PCV

37. Conclusions • DATA-MC: the agreement is satisfying; systematics and BR seems to be under control: we are recomputing • For us everithing is ok and we are ready to produce final number and documentation, if we are below 1%: TOF systematic still to be studied. • Kinematic fit does not improve resolution and background reduction, moreover the c2 distribution shows disagrement at 2-5% level (well beyond our target) • We can apply PCV requirement, in RAD stream, we do not use extra VTX info; use Roberto and Antonio work on tracks/vtx

38. Spare

39. Fitting with fraction fitter seems better (but fit probability still low)

40. E