1. TOF, Status of the Code F. Pierella, Bologna University and INFN TOF Offline Group ALICE Offline Week, June 2002

2. For participants in virtual rooms URL for this presentation Explorer http://www.bo.infn.it/alice/pierella/Doc/June02E.html Netscape http://www.bo.infn.it/alice/pierella/Doc/June02.html

3. Contents Activity during the past 2 month: Geometry SDigitization Merging/Digitization " CPU time estimation for Sdigitization and Merging Reconstruction/PID QA and test macros Probabilities for PID Propagation from TPC to TOF using Kalman AliTOFV2 AliTOFV3 Conclusions & outlook on 'time zero'

4. Geometry Review on TOF geometry Some volume overlaps has been fixed; review on materials. Cooling tubes and FE card has been introduced in the GEANT description of the TOF detector

5. Geometry (2) Proof

6. Sdigitization (1) UML diagram: ClassDef(AliTOFSDigitizer,2)

7. Sdigitization (2) Summary Class: AliTOFSDigitizer Inherits from TTask Output: TClonesArray of AliTOFSDigit " The AliTOF fSDigits data member is transient QA and test macros: " AliTOFhits2SDigits.C " AliTOFanalyzeSDigitsV2.C " AliTOFanalyzeSDigits.C (to be used if fSDigits is persistent)

8. Sdigitization (3) TDC distribution (1 TDC bin= 50ps) as an example (25 central HIJING events in the theta range [45°-135°])

9. Merging/Digitization (1) Algorithm description: Sdigits from different files (e.g. for BKG and SGN) are merged (i.e. 'summed' if necessary, using the AliTOFHitMap) and collected in a tmp array; from this array they are converted into TOF digits. No noise added (for the time being) due to the negligeable expected noise level of 1Hz/pad: " taking into account a readout window of 500ns (and the total number of readout channels) the expected noise value is 0.08

10. Merging/Digitization (2) Summary: Class: AliTOFDigitizer Inherits from AliDigitizer Output: TClonesArray of AliTOFdigit QA and test macros: " AliTOFSDigits2Digits.C (only digitization, no merging) " AliTOFanalyzeDigits.C

11. Merging/Digitization (3) UML diagram

12. CPU time estimation for sdigitization and merging Sdigitization ~3s/ event  LEGENDA: event=central Hijing event in theta range [45°,135°] Digitization only ~1s/event

13. Reconstruction/PID QA and test macros Reconstruction Class: AliTOFReconstructioner Inherits from TTask Output: TNtuple object (assignment of time of flight to tracks) QA and test macros: " AliTOFtestRecon.C " AliTOFanalyzeMatchin g.C PID ('last step' efficiency data added) Class: AliTOFPID Inherits from TTask Output: TH1F objects QA and test macros: " AliTOFtestPID

14. Probabilities for PID (1) Definition of probability from TOF-PID (Hijing)

15. Probabilities for PID (2) the same but for Shaker (different 'model' -> different amplitudes) (how to avoid model dependency in defining probability?)

16. Probabilities for PID (3) (Sigmas comparison in Shaker & Hijing) Hijing Unit [MeV/c*c]  Pions :  (m)~90  Kaons:  (m)~56  Protons:  (m)~33 Shaker Unit [MeV/c*c]  Pions :  (m)~90  Kaons:  (m)~53  Protons:  (m)~32

17. Probability to be pion 1.5GeV/c<p<2.GeV/c (Pb-Pb Hijing)

18. Probability to be pion (2) 1.5GeV/c<p<2.GeV/c (pp, PYTHIA)

19. Probability to be kaon 1.5GeV/c<p<2.GeV/c (Pb-Pb) (fit problem)

20. Probability to be kaon (2) 1.5GeV/c<p<2.GeV/c (pp)

21. Probability to be proton 1.5GeV/c<p<2.GeV/c (fit problem)

22. Probabilities for PID (2)... in different momentum range

23. Propagation from TPC to TOF using Kalman This exercise started before the TRD tracking was ready We plan to use the backpropagation from TRD to TOF detector (very short distance compared to the previous TPC->TOF) Preliminary results for the area spread by the track propagation (it results less than the statistical method -see TOF TDR Addendum Chapter 5, Section 5.5-)

24. Propagation from TPC to TOF using Kalman (2) From TPC reconstructed tracks (and back propagated in TPC!) I step : propagation through the outer wall of the TPC (radiation length from TPC TDR) II step: propagation in air (for the time being, applied to events with no TRD) III step: propagation through the outer wall of the TOF IV step: derive the area spread by the track  Area=3  (y)*3  (z)

25. Back Propagation in TPC Area after back propagation in TPC

26. Propagation from TPC to TOF using Kalman (4) Area after propagation to TOF(~4 TOF pads)

27. AliTOFT0V2 (1) Algorithm description: Combinatorial method as described in TOF TDR Addendum (Chapter 5, Section 5.7) BUT, now applied to reconstructed tracks (i.e. including also the tracks with a wrong time of flight assignment) - with p>1GeV/c, to have the larger matching efficiency - In any case (preliminary!) a better resolution than 50ps can be reached And, (no surprise!) by using the library (not an interpreted code as in the past) the computing time is reduced by a factor 10.

28. AliTOFT0V2 (2) Preliminary result for time zero (B=0.4T)

29. AliTOFT0V2 (3) Same as previous slide but at B=0.2T

30. AliTOFT0V3 (1) Implementation of the following idea: "Assume for all (high statistics) reconstructed tracks the pion mass and derive the time zero by meaning the zero time of all tracks" Preliminary result: the zero time mean distribution is narrow (~60ps) BUT it is not centered around zero (as it as to be in MC) due to the systematic wrong mass assumption (need for truncated mean analysis).

31. AliTOFT0V3 (2) Preliminary result for 100 HIJING events

32. AliTOFT0V3 (3) Preliminary result for 100 SHAKER events

33. Conclusions & Outlook on 'time zero' Several ideas for 'time zero' determination are under investigation the most interesting and fascinating one is the following: " Take the earliest signals on TOF (they are mainly due to electrons from prompt gamma conversion in the TOF volume - to be verified and how to tag them?-> may be using TOF signals not matched with the TRD reconstructed tracks-) " Use a straight line approximation, assume as velocity the speed of the light -as in the gamma case - and derive the 'time zero' " No reconstruction needed at all