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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
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
Particle identificationParticle identification
1 Simple contour cut
2 Neural network
3 Probability approach
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
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)
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