1. Algorithms and Methods for Particle Identification with ALICE TOF Detector at Very High Particle Multiplicity TOF simulation group B.Zagreev ACAT2002, 24 June 2002

2. ALICE Time-Of-Flight detector (TOF) R=3.7m, S=100m 2, N=160000

3. Problems Need of very high time resolution (60 ps - intrinsic, 120 ps - overall) High multiplicity dN/dY  8000 primaries (12000 particles in TOF angular acceptance) –45(35)% of them rich TOF, but they produce a lot of secondaries High background –total number of fired pads ~ 25000 => occupancy=25000/160000=16% –but only 25% of them are fired by particles having track measured by TPC Big gap between tracking detector (TPC) and TOF –big track deviation due to multiple scattering –TRD tracking ???

4. Procedure Software framework for ALICE - Aliroot (ROOT based + GEANT3). Then we have the same environment for simulation and reconstruction. –Tracking (Kalman filtering) –Matching –Time measurements –Particle identification

5. Matching Probe tracks algorithm Kalman filtering Combined method (Kalman + probe tracks)

6. Probe tracks algorithm All tracks are ordered according their transverse momentum (the higher momentum the less track errors) Starting from the highest momentum track, for each track at the outer layer of TPC, a statistically significant sample of probe tracks is generated and tracked in Aliroot (GEANT geometry and medias, magnetic field etc.) So for a given track we have a set of TOF pads crossed by these probe tracks. We chose, roughly, the pad crossed by biggest number of probe tracks.

7. Probe tracks algorithm The end of reconstructed track (  r,  p) in TPC or TRD Fired pads

8. Kalman filtering + probe tracks algorithm The ends of reconstructed track (  r,  p) S2S2 33 S1S1 R1R1 R2R2 R 1 <R 2 but S 1 <S 2 ! TPC (TRD) TOF

9. Time measurements Time-amplitude and other corrections Time zero calculations

10. 1. Consider a very small subset (n) of primary “gold” tracks. Let l 1 …l n, p 1 …p n, t 1 …t n - length, momentum and time of flight of corresponding tracks. Now we can calculate the velocity (v i ) of particle i in assumption that particle is pion, kaon or proton. 2. Then we can calculate time zero: 3. We chose configuration C with minimal  2 (C) ~  (t i 0 (C) - (C)) 2 Combinatorial algorithm for t0 calculation

11. Combinatorial t0 distribution (250 events)

12. Results for t0 combinatorial algorithm Now 30sec (PIII)

13. t0 calculation, all tracks as pions

14. T0 calculations with not matched hits

15. Particle identification 1Simple contour cut 2Neural network 3Probability approach

16. Mass distribution, 50 HIJING events, B=0.4T

17. Mass-momentum distribution, HIJING

18. TOF efficiency and contamination

19. Neural network PID ROOT based network constructor (Anton Fokin, http://www.smartquant.com/neural.html) 1 hidden layer perceptron (different number of neurons) output: 3 neurons for , K or p input parameters: mass, momentum and matching parameter Good results for not overlapping clusters of particles. For realistic distribution performance is not so good

20. Mass-momentum distribution, HIJING neurons

21. Fit with 2D function

22. Probabilities for PID, (1.5-2 GeV/c) 50% 70%

23. PID at STAR experiment  e K p

25. x Combine PID pions kaons y g K (x,y)~g K (x)g K (y) 1D cuts 2D cut g K (x) g K (y)

26. Conclusions & plans A number of methods and algorithms were developed for particle identification at high multiplicity and background Results obtained are reasonable and allow to fulfil physical tasks Plans: –Complete probability algorithm, combine several detectors –Kalman filtering for matching –Try to realize iterative algorithm for tracking, matching and particle identification