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

1
Algorithms and Methods for Particle
Identification with ALICE TOF Detector at Very
High Particle Multiplicity

• TOF simulation group
• B.Zagreev
• ACAT2002, 24 June 2002

2
ALICE Time-Of-Flight detector (TOF)R3.7m,
S100m2, N160000
3
Problems

• Need of very high time resolution (60 ps -
  intrinsic, 120 ps - overall)
• High multiplicity dN/dY?8000 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
  gt
  occupancy25000/16000016
• 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 ???

4
Procedure

• 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

5
Matching

• Probe tracks algorithm
• Kalman filtering
• Combined method (Kalman probe tracks)

6
Probe 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.

7
Probe tracks algorithm
Fired pads
The end of reconstructed track (?r, ?p) in TPC or
TRD
8
Kalman filtering probe tracks algorithm
R1ltR2 but S1ltS2 !
S1
S2
R1
3?
R2
TOF
The ends of reconstructed track (?r, ?p)
TPC (TRD)
9
Time measurements

• Time-amplitude and other corrections
• Time zero calculations

10
Combinatorial algorithm for t0 calculation

• 1. Consider a very small subset (n) of primary
  gold tracks. Let l1ln, p1pn, t1tn - 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) ? (ti0(C) - ltti0gt(C))2

11
Combinatorial t0 distribution (250 events)
12
Results for t0 combinatorial algorithm
Now 30sec (PIII)
13
t0 calculation, all tracks as pions
14
T0 calculations with not matched hits
15
Particle identification

• Simple contour cut
• Neural network
• Probability approach

16
Mass distribution, 50 HIJING events, B0.4T
17
Mass-momentum distribution, HIJING
18
TOF efficiency and contamination
19
Neural 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

20
Mass-momentum distribution, HIJING
21
Fit with 2D function
22
Probabilities for PID, (1.5-2 GeV/c)
70
50
50
23
PID at STAR experiment
p
e
K
?
24
(No Transcript)
25
Combine PID
y
gK(x,y)gK(x)gK(y)
1D cuts
gK(y)
kaons
pions
2D cut
gK(x)
x
26
Conclusions 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