D. Di Bari 1 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Methods of Cherenkov pattern recognitions in high multiplicity environments HMPID in the ALICE exp. at LHC pattern recognition and RICH performance charged particle ID on real events (STAR) novel developments D. Di Bari - University of Bari & iNFN

D. Di Bari 2 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Pysics Motivation u ALICE is a multi-purpose experiment m aim: study the properties of the Quark Gluon Plasma  HMPID: to identify charged particles with high p T in the central rapidity region (|  | < 1)  1 < p T < 3 GeV/c  / K â 1.5 < p T < 6 GeV/c p u Sub-detectors designed for Pb-Pb collisions at  s=5.5 TeVA with anticipated multiplicity dN/dy ~ 8000  10-15% pad occupancy for RICH ( part/m 2 )

D. Di Bari 3 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece

D. Di Bari 4 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece u Conversion of photons in m CsI (QE 170 nm) u Radiator m C 6 F 14 Liquid u Photo Detector m MWPC pad chamber m CH 4 Gas Proximity Focusing

D. Di Bari 5 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece GEANT 3.21 Simulation  = rings Momentum from TPC Matching between extrapolated point and MIP on the RICH TPC ITS RICH track quartz CH 4 pad plane

D. Di Bari 6 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Detector response MIP photons cos  c = 1/n  Npad (MIP)  5  6 Npad (photons)  2 MWPC HV = 2100 V operated with CH 4 raw photon clusters/ring = 14.8 res. photon clusters/ring = 16.2

D. Di Bari 7 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Simulation with ALIROOT (C++)

D. Di Bari 8 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Pattern recognition in ALICE The Hough 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) cluster coordinate impact track parameter solution in one dimensional mapping space  c photon Cerenkov angle  (x,y)  ((x p,y p,  p,  p ),  c )

D. Di Bari 9 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece  c = reconstructed theta Cherenkov for each photon  c = reconstructed theta Cherenkov per particle geometrical backtracing = reconstruction of the angle under which the “candidate” photon could have been emitted quartz window C 6 F 14 CH 4 MIP photon cluster proximity gap radiator incoming particle

D. Di Bari 10 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece u background estimate m hypotesis  background uniformly distrib. â the photons falling in opening bands of 10 mrad are weighted for the corresponding band area simulation pions in saturation (  = 1) MIP calculated area weight = 1/area

D. Di Bari 11 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece N end -N star Improvement of (  track,  track )  after having determined the photon candidates, a minimization of the rms/  N of the photon distr. has been performed with (  track,  track ) as free parameters  with the new fitted  track,  track the Hough transform is again applied stop if the # photons remains the same !  track  track end start

D. Di Bari 12 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece...at the end of the iteration photon included after the (  track  track ) correction

D. Di Bari 13 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Efficiency and contamination Efficiency and contamination as a function of the track momentum (dN/dy = 8000)

D. Di Bari 14 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece installation of the proto-2 in the STAR experiment: unique opportunity to test the detector 5(?) years before the ALICE start

D. Di Bari 15 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece positives + negatives Reconstructed Theta Cherenkov vs. track momentum in STAR p th  1.26 m GeV/c

D. Di Bari 16 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Sample of events with track of p >1GeV/c |  | < 0.15 p K  physics analysis

D. Di Bari 17 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece 2 < p t < 2.5 GeV/c protons poissonian distrib. Evaluation of N sat sin 2  c sin 2  c,sat =  Nsat = 5.6/0.677 = 8.5 Theta Cherenkov (rad) cluster multiplicity Nph = 9 Nph = CERN test beam (in 2000 data lower gain)

D. Di Bari 18 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Tuning of the n(,T) mass (GeV) momentum (GeV/c)  n/  T  / ºC momentum (GeV/c) mass (GeV) m = P TPC /(  RICH pions kaons protons p > 1 GeV

D. Di Bari 19 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Fitted peak positions for  /K/p in agreement with the expected  Cherenkov vs. p t curves  /K and K/p separation as function of p t  

D. Di Bari 20 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece p K-K-   K+K+ p positives negatives The signal of ,  and p could be extracted by fitting the distribution of reconstructed  Cherenkov in different p T ranges Signal extraction

D. Di Bari 21 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece Charged particle ratios with RICH in STAR not corrected for acceptance efficiency

D. Di Bari 22 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greececandidatesidentifiedefficiency(%) err (%) protons antiprotons Proton Identification efficiency  (Anti)Proton identification efficiency has been evaluated by the identification of the (anti) protons coming from the charged dacay of (anti)lambda  p  - overall efficiency in the range 1.5 < p T < 2.5 GeV/c

D. Di Bari 23 IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece u SVM is a new (1995) and promising classification technique with high generalization power. u 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. Support Vector Machines 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 RICH Classification problem: pions/kaons/protons discrimination. Input space: photon Cherenkov angles. Output space: class membership probability.