Algorithms and Methods for Particle Identification with ALICE TOF Detector at Very High Particle

Multiplicity

Algorithms and Methods for Particle Identification with ALICE TOF Detector at Very High Particle

Multiplicity

TOF simulation group

B.Zagreev

ACAT2002, 24 June 2002

ALICE Time-Of-Flight detector (TOF)R=3.7m, S=100m2, N=160000

ALICE Time-Of-Flight detector (TOF)R=3.7m, S=100m2, N=160000

ProblemsProblems• Need of very high time resolution (60 ps - intrinsic, 120 ps -

overall)

• High multiplicity dN/dY8000 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 ???

ProcedureProcedure

• 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

MatchingMatching

• Probe tracks algorithm

• Kalman filtering

• Combined method (Kalman + probe tracks)

Probe tracks algorithmProbe 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.

Probe tracks algorithmProbe tracks algorithm

The end of reconstructed track (r, p) in TPC or TRD

Fired pads

Kalman filtering + probe tracks algorithm

Kalman filtering + probe tracks algorithm

The ends of reconstructed track (r, p)

S2

3

S1

R1

R2

R1<R2 but S1<S2 !

TPC (TRD)

TOF

Time measurementsTime measurements

• Time-amplitude and other corrections

• Time zero calculations

1. Consider a very small subset (n) of primary “gold” tracks. Let l1…ln, p1…pn, t1…tn - length, momentum and time of flight of corresponding tracks. Now we can calculate the velocity (vi) 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) ~ (ti

0(C) - <ti0>(C))2

Combinatorial algorithm for t0 calculation

ii

ii t

v

lt

p)K,,( 0

Combinatorial t0 distribution (250 events)Combinatorial t0 distribution (250 events)

Results for t0 combinatorial algorithm

Results for t0 combinatorial algorithm

Now 30sec (PIII)

t0 calculation, all tracks as pionst0 calculation, all tracks as pionstpmltclt 1// 22

0

T0 calculations with not matched hitsT0 calculations with not matched hits

Particle identificationParticle identification

1 Simple contour cut

2 Neural network

3 Probability approach

Mass distribution, 50 HIJING events, B=0.4TMass distribution, 50 HIJING events, B=0.4T

1/ 222 ltcpm

Mass-momentum distribution, HIJINGMass-momentum distribution, HIJING1/ 222 ltcpm

TOF efficiency and contaminationTOF efficiency and contamination

Neural network PIDNeural 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

Mass-momentum distribution, HIJINGMass-momentum distribution, HIJING1/ 222 ltcpm

neurons

neurons

Fit with 2D functionFit with 2D function

Probabilities for PID, (1.5-2 GeV/c) Probabilities for PID, (1.5-2 GeV/c)

50%

50%

70%

PID at STAR experimentPID at STAR experiment

e Kp

12 March 2002 Karel Safarik: ALICE Performance 17

Particle IdentificationParticle Identification /K K/p/K K/p

TPC and ITSTPC and ITS ( (dEdE//dxdx))

/K K/p/K K/p

TOFTOF

/K K/p/K K/p

HMPIDHMPID (RICH) (RICH)

e/ e/TRDTRD

//

PHOSPHOS

Muon Muon detectordetector

0 1 2 3 4 5 p (GeV/c)

1 10 100 p (GeV/c)

x

Combine PIDCombine PID

pions

kaons

y

gK(x,y)~gK(x)gK(y)1D cuts

2D cut

gK(x)

gK(y)

Conclusions & plansConclusions & 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