Methods of Cherenkov pattern recognitions in high multiplicity environments

D. Di BariD. Di Bari 1IV Workshop on RICH Detectors, 5-10 June 2002 Pylos, Greece

Methods of Cherenkov pattern Methods of Cherenkov pattern recognitions in high multiplicity recognitions in high multiplicity

environmentsenvironments

•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


Pysics MotivationPysics Motivation ALICEALICE is a is a multi-purposemulti-purpose experiment experiment

aim: study the properties of the Quark Gluon Plasmaaim: study the properties of the Quark Gluon Plasma

HMPIDHMPID: to identify : to identify charged particlescharged particles with high p with high pTT in the in the

central rapidity region (central rapidity region (||| < 1| < 1)) 1 < p1 < pTT < 3 GeV/c < 3 GeV/c //KK

1.5 < p1.5 < pTT < 6 GeV/c < 6 GeV/c pp

Sub-detectors designed for Pb-Pb collisions at Sub-detectors designed for Pb-Pb collisions at s=5.5 s=5.5 TeVA with anticipated multiplicity TeVA with anticipated multiplicity dN/dy dN/dy ~ ~ 80008000

10-15% pad occupancy for RICH (80-100 part/m10-15% pad occupancy for RICH (80-100 part/m22))



Conversion of photons inConversion of photons in

CsI (QE 20% @ 170 nm)CsI (QE 20% @ 170 nm) RadiatorRadiator

CC66FF1414 Liquid Liquid Photo DetectorPhoto Detector

MWPC pad chamberMWPC pad chamber CHCH44 Gas Gas

Proximity Focusing


GEANT 3.21 Simulation

= 1 100 rings

• Momentum from TPC• Matching between extrapolated point and MIP on the RICH

TPC

ITS

RICHtr

ack

quartzCH4

pad

pla

ne


Detector responseDetector response

MIP

photons

coscoscc = 1/n = 1/n

Npad (MIP) 56

Npad (photons) 2

MWPC HV = 2100 Voperated with CH4

• raw photon clusters/ring = 14.8• res. photon clusters/ring = 16.2


Simulation with ALIROOT (C++)


Pattern recognition in Pattern recognition in ALICEALICE

TheThe Hough Transform MethodHough Transform Method ( (HTMHTM) represents an ) represents an efficient efficient implementation of a generalized implementation of a generalized template matchingtemplate matching strategy for strategy for detecting complex patterns in binary images (looking for detecting complex patterns in binary images (looking for local local

maximamaxima in a in a feature parameterfeature parameter space) space)

cluster coordinate

impact track parameter

solution in one dimensional

mapping space c

photon Cerenkov

angle

(x,y) ((xp,yp,p,p), c)


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

C6F14

CH4

MIP

photon cluster

proximity gapradiator

incomingparticle


background estimatebackground estimate hypotesis hypotesis background background uniformly distrib.uniformly distrib.

the photons falling in opening bands of 10 mrad are the photons falling in opening bands of 10 mrad are weighted weighted for the corresponding band areafor the corresponding band area

simulation

pions in saturation( = 1)

MIP

calculated area

weight = 1/area


0

12-1

Nend-Nstar

Improvement of (Improvement of (tracktrack,,tracktrack)) after having determined the photon candidates, a after having determined the photon candidates, a

minimizationminimization of the of the rms/rms/NN of the photon distr. has been of the photon distr. has been performed with (performed with (tracktrack,,tracktrack) as ) as free parametersfree parameters

with the new fitted with the new fitted tracktrack,,track track the Hough transform is the Hough transform is

again again applied applied

stop if the # stop if the # photons remains photons remains

the same !the same !

tracktrack

end start


...at the end of the iteration

photon included after the (photon included after the (tracktracktracktrack) correction) correction


Efficiency and Efficiency and contaminationcontamination

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


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


positives+

negatives

Reconstructed Theta Cherenkov vs. Reconstructed Theta Cherenkov vs. track momentum in STARtrack momentum in STAR

pth 1.26 m GeV/c


Sample of events with track of p Sample of events with track of p >1GeV/c>1GeV/c

|| < 0.15

p

K

physics analysis


2 < pt < 2.5 GeV/c

protons

poissonian distrib.

Evaluation of NEvaluation of Nsatsat

sin2c

sin2c,sat

= 0.677

Nsat = 5.6/0.677 Nsat = 5.6/0.677 = 8.5 = 8.5

Theta Cherenkov (rad)

clu

ste

r m

ult

iplicit

y

Nph = 9

Nph = 15 @ CERN test beam (in 2000 data lower gain)


Tuning of the n(Tuning of the n(,T) ,T) m

ass

(G

eV

)

momentum (GeV/c)

n/T -0.0005 / ºC

momentum (GeV/c)

mass

(G

eV

)

m = PTPC/(RICHpions

kaonsprotons

p > 1 GeV


Fitted peak positions for /K/pin agreement with the expected

Cherenkov vs. pt curves

/K and K/p separation as function of pt


pK-

K+

p

positives

negatives

The signal of , and p could be extracted by

fitting the distribution of reconstructed Cherenkov in

different pT ranges

Signal extractionSignal extraction


Charged particle ratios Charged particle ratios with RICH in STARwith RICH in STAR

not corrected for •acceptance•efficiency


candidatescandidates identifiedidentified efficiencyefficiency(%)(%)

err (%)err (%)

protonsprotons 157157 126126 80.380.3 3.23.2

antiprotonsantiprotons 8181 6464 79.079.0 4.54.5

Proton Identification Proton Identification efficiencyefficiency

(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 < pT < 2.5 GeV/c


SVMSVM is a is a newnew (1995) and promising (1995) and promising classification technique with high classification technique with high generalization power.generalization power.

It is particularly apts with complex images. It is particularly apts with complex images. Basic idea: separate the classes with a Basic idea: separate the classes with a surface that surface that maximizesmaximizes the margin between the margin between them and them and minimizeminimize the error in the the error in the misclassification of data.misclassification of data.

Support Vector Support Vector MachinesMachines

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.

RICH Classification problem: pions/kaons/protons discrimination.

Input space: photon Cherenkov angles.

Output space: class membership probability.

Documents

Methods of Cherenkov pattern recognitions in high multiplicity environments