Methods of Cherenkov pattern recognitions in high multiplicity environments

1. Methods of Cherenkov pattern recognitions in high multiplicity environments D. Di Bari - University of Bari & iNFN • HMPID in the ALICE exp. at LHC • pattern recognition and RICH performance • charged particle ID on real events (STAR) • novel developments

2. Pysics Motivation • ALICE is a multi-purpose experiment • aim: study the properties of the Quark Gluon Plasma • HMPID: to identify charged particles with high pT in the central rapidity region (|h| < 1) • 1 < pT < 3 GeV/c p/K • 1.5 < pT < 6 GeV/c p • Sub-detectors designed for Pb-Pb collisions at s=5.5 TeVA with anticipated multiplicity dN/dy ~ 8000  10-15% pad occupancy for RICH (80-100 part/m2)

4. Proximity Focusing • Conversion of photons in • CsI (QE 20% @ 170 nm) • Radiator • C6F14 Liquid • Photo Detector • MWPC pad chamber • CH4 Gas

5. GEANT 3.21 Simulation RICH b = 1 100 rings TPC ITS pad plane track CH4 quartz • Momentum from TPC • Matching between extrapolated • point and MIP on the RICH

6. cosqc = 1/nb photons MIP MWPC HV = 2100 V operated with CH4 Npad (MIP)  56 • raw photon clusters/ring = 14.8 • res. photon clusters/ring = 16.2 Npad (photons) 2 Detector response

7. Simulation with ALIROOT (C++)

8. Pattern recognition in ALICE TheHough Transform Method (HTM) represents an efficient implementation of a generalized template matching strategy for detecting complex patterns in binary images (looking for local maxima in a feature parameter space) (x,y)  ((xp,yp,qp,jp), hc) cluster coordinate photon Cerenkov angle impact track parameter  solution in one dimensional mapping space hc

9. hc= reconstructed theta Cherenkov for each photon qc = reconstructed theta Cherenkov per particle geometrical backtracing = reconstruction of the angle under which the “candidate” photon could have been emitted quartz window photon cluster C6F14 incoming particle MIP CH4 radiator proximity gap

10. background estimate hypotesis  backgrounduniformly distrib. the photons falling in opening bands of 10 mrad are weighted for the corresponding band area pions in saturation (b = 1) simulation MIP calculated area weight = 1/area

11. Improvement of (qtrack,jtrack) • after having determined the photon candidates, a minimization of the rms/N of the photon distr. has been performed with (qtrack,jtrack) as free parameters • with the new fitted qtrack,jtrack the Hough transform is again applied 0 stop if the # photons remains the same ! start end qtrack - qtrack Nend-Nstar 1 -1 2

12. ...at the end of the iteration photon included after the (qtrack,jtrack) correction

13. Efficiency and contamination Efficiency and contamination as a function of the track momentum (dN/dy = 8000)

14. installation of the proto-2 in the STAR experiment: unique opportunity to test the detector 5(?) years before the ALICE start installation of the proto-2 in the STAR experiment: unique opportunity to test the detector 5(?) years before the ALICE start

15. Reconstructed Theta Cherenkov vs. track momentum in STAR positives + negatives pth 1.26 m GeV/c

16. p K p || < 0.15 physics analysis Sample of events with track of p >1GeV/c

17. Evaluation of Nsat protons poissonian distrib. sin2qc 0.677  = sin2qc,sat Nsat = 5.6/0.677 = 8.5 2 < pt < 2.5 GeV/c Nph = 9 cluster multiplicity Theta Cherenkov (rad) Nph = 15 @ CERN test beam (in 2000 data lower gain)

18. Tuning of the n(l,T) m = PTPC/(bg)RICH p > 1 GeV pions kaons protons mass (GeV) Dn/DT  -0.0005 / ºC momentum (GeV/c) mass (GeV) momentum (GeV/c)

19. Fitted peak positions for /K/p in agreement with the expected Cherenkov vs. ptcurves  /K and K/pseparation as function of pt 

20. Signal extraction The signal of p, K and p could be extracted by fitting the distribution of reconstructed Cherenkovin different pTranges negatives p- K- p positives p+ K+ p

21. Charged particle ratios with RICH in STAR • not corrected for • acceptance • efficiency

22. Proton Identification efficiency p - • (Anti)Proton identification efficiency has been evaluated by the identification of the (anti) protons coming from the charged dacay of (anti)lambda overall efficiency in the range 1.5 < pT < 2.5 GeV/c

23. Support Vector Machines • SVM is a new (1995) and promising classification technique with high generalization power. • It is particularly apts with complex images. Basic idea: separate the classes with a surface that maximizes the margin between them and minimize the error in the misclassification of data. • RICH Classification problem: pions/kaons/protons discrimination. • Input space: photon Cherenkov angles. • Output space: class membership probability. References: 1) E.E. Osuna, R. Freud, F. Girosi, Support Vector Machines: Training end Applications MIT, (1997). 2) M. Feindt, C. Haag, DELPHI Collaboration, Support Vector Machines for Classification Problems in High Energy Physics, Institute fur experimentelle Kernphysik, Universitat Karlsruhe, (1999). 3) L. Maglietta, “Support vector machines for electron/antiproton discrimination by a transition radiation detector” (Pamela exp.), Università degli Studi di Bari, Thesis Degree, march 2002.