PID Capabilities using Artificial Neural Networks in the TRD

1
PID Capabilities using Artificial Neural Networks
in the TRD
Alexander Wilk IKP Münster August 22nd 2008 ALICE
Workshop Sibiu
2
gt Outline

• PID methods for the TRD and their principles
• performance results
• test beams
• simulations
• electrons from g-conversions as reference data
• contamination studies
• summary

3
gt Definitions

• design goal for the ALICE TRD PID
• pion efficiency lt 1 at 90 electron
  efficiency
• electron efficiency is the rate of electrons
  which are identified correctly
• pion efficiency is the rate of pions which are
• misidentified as electrons
• pion suppression is the reciprocal of pion
  efficiency
• non-electron efficiency is the same as pion
  efficiency, but the pions here are a mixture
  of hadrons

4
gt non-Electron Efficiency - Particle
Ratios in pp Collisions

• ratios of primary particles are taken into
  account for the calculation of the efficiencies
• negative sample
• is a mixture of all other particle types,
  depending on their ratios in pp collisions

e
m
10
10
0
0
K
p
10
10
0
0
1
p
ratio
0
0
10
0
10
pT (GeV/c)
pT (GeV/c)
5
I. PID Methods
6
gt Principles of PID in the TRD

• TRD signal is composed of 2 components
• energy loss of charged particles
• (all particles)
• absorption of TR photons
• (g gt 1000, electrons only)
• transition radiation photon absorbed early
• in chamber ? late in drift time
• signal in one chamber correlated
• in time (TRF), but independent between
• different chambers
• 6 layers of TRD chambers
• 24 time measurements per chamber
• ? 6 x 24 144 measurements per track

TR Peak
7
gt 2-dimensional Likelihood (A. Bercuci, GSI)

• information of 2 time slices per chamber are
  connected in one histogram
• idea
• signal of electrons should be significantly
• larger in the second time slice due to
• transition radiation
• advantages
• consideration of time information
• consideration of correlations between
• different time slices
• disadvantages
• not extendable to more than 3 time bins
• (statistical problem)

1
2
http//indico.cern.ch/getFile.py/access?resId0ma
terialId slidescontribIdI9sessionId2subContI
d6confId7104
8
gt Artificial Neural Networks

• information of more time slices is usable (here 8
  slices)
• time information is not lost
• network topology (for 1 chamber)
• 8 (or more) input neurons
• 2 hidden layer
• 2 or 5 output neurons
• calculation of particle probabilities for 6
  chambers analogue to likelihood method

Input
http//indico.cern.ch/getFile.py/access?contribId
10resId1materialIdslidesconfId20791
9
gt Artificial Neural Networks

• information of more time slices is usable (here 8
  slices)
• time information is not lost
• network topology (for 1 chamber)
• 8 (or more) input neurons
• 2 hidden layer
• 2 or 5 output neurons
• calculation of particle probabilities for 6
  chambers analogue to likelihood method

e m p K p
Input
http//indico.cern.ch/getFile.py/access?contribId
10resId1materialIdslidesconfId20791
10
gt Artificial Neural Networks

• information of more time slices is usable (here 8
  slices)
• time information is not lost
• network topology (for 1 chamber)
• 8 (or more) input neurons
• 2 hidden layer
• 2 or 5 output neurons
• calculation of particle probabilities for 6
  chambers analogue to likelihood method

e m p K p
Input
,
http//indico.cern.ch/getFile.py/access?contribId
10resId1materialIdslidesconfId20791
11
gt Test Beam Times

• 3 larger test beam times at CERN PS
• 2002 (prototype tests)
• 2004 (stack of 6 large chambers)
• 2007 (super module III)
• secondary beam composed of electrons and pions
• momenta between 1 GeV/c and 10 GeV/c
• independent PID using a Cherenkov detector and a
  PbGl calorimeter

12
gt Test Beam Time 2002 - Comparison 1-dim LQ/NNs

• first application of artificial neural networks
  in the TRD
• 2002 extrapolation from 4 to 6 chambers
• design goal reached using 1-dim likelihood and
  NNs
• NNs give (factor 3) better results than LQ
• NNs exploit time
• information

design goal for 3 GeV/c
13
gt Test Beam Times Overview - Results NNs

• later beam time results demonstrate NNs advantage
  compared to 1-dim likelihood
• unfortunately results from 2002 not completely
  reached

design goal for 3 GeV/c
14
gt Test Beam Times Overview - Number of Input
Neurons

• rising number of input neurons
• better pion efficiency

Slices
number of input neurons
15
gt PID in Simulations

• goal implementation of PID using NNs in AliRoot
  and building of reference data
• for training of the networks no complete
  simulation of collisions necessary
• simulation of
• 5 particle types (e, m, p, K, p)
• 11 momenta (0.6, 0.8, 1.0, 1.5, 2.0, 3.0, 4.0,
  5.0, 6.0, 8.0 und 10.0 GeV/c)
• 20 particles and anti-particles per particle type
  in each event (200 particles per event)

16
gt Results from Simulations

• 2-dim likelihood and
• NNs significantly better than 1-dim LQ
• NNs about a factor of 2 better than 2-dim
  likelihood

17
gt Results for Hadron PID

• TRD can help with hadron identification at small
  momenta

18
gt Summary of the PID methods

• particle identification in the TRD is based on
  the deposited charge of the crossing particle and
  the absorption of created transition radiation
• two powerful methods were developed and
  implemented in AliRoot
• 2-dim likelihood method
• artificial neural networks
• suppression factor for hadrons is about 1000
• for 2 GeV/c in simulations

19
II. Reference Data
20
gt Types of Reference Data

• simulations
• at the moment used in AliRoot
• special PID simualtions using 5 particle types
  with a given momentum
• test beam data 2007
• no magnetic field (no Lorentz angle)
• only one beam position in the SM and only one
  incident angle
• problems with the PID not usable as reference
  data for NNs (?)
• reference data from real data needed!

21
gt Particles from Displaced Vertices (M. Heide,
IKP)

• use particles from displaced vertices in pp
  collisions as reference
• (e.g. g-gtee-)
• independent PID information
• reconstruction of invariant mass
• location of the decay
• additional PID of the other barrel detectors
  (ITS, TPC and TOF)
• study done for TPC dE/dx calibration (A.
  Kalweit, GSI)

http//indico.cern.ch/getFile.py/access?contribId
4resId 1materialIdslidesconfId8438
22
gt Example g Conversions

• probability for g conversions in the inner barrel
  10
• reconstruction in AliRoot via the V0 finder

23
gt Spectrum of Invariant Mass

• spectrum after cuts on
• pointing angle lt 0.03
• TRD hits 6
• clear peak at minv 0
• small background
• electrons also parts of the background

all reconstructed particles reconstructed
g background
24
gt pT Spectrum of the Electrons

• pT spectrum after cuts
• pointing angle lt 0.03
• TRD hits 6
• TPC PID gt 0.5
• small background 20 non electrons
• statistics for 400k events

e from reconstructed g e in background non e
background
25
gt Summary Reference Data

• reference data from real data taking needed for
• 2-dim LQ reference histograms
• training of artificial neural networks
• very pure samples of electrons can be extracted
  for momenta lt 3 GeV/c
• to do
• study with large statistics simulations of g
  conversions with high pT (TPC PID)
• extract ps and ps from K0s and Ls
• think about kaons

26
III. Contamination Studies
27
gt Contamination Studies - Motivation

• pT spectrum of particles reconstructed with
  V0-finder (no cuts)
• background can be reduced by a very large factor
  (depending on cuts)
• how much background is acceptable for training
  the NNs?

background e from reconstructed g electrons in
background
(Markus Heide, IKP)
28
gt Contamination of the Samples

• contamination is the rate of not correctly
  identified (or assigned) particles
• particles in the contamination are a mixture of
  particles given by the rates of different
  background particles (extracted by Markus Heide)
• contamination is done using gRandom
• if contamination is done (depending on cont.
  level)
• which particle type is used (depending on
  particle ratios)
• only training samples contaminated, test samples
  consist of pure particle samples

29
gt Example Contamination 30
each sample consists of 70 correct assigned
particles 30 background
background (2GeV/c) e 5.8 m 0.5
p 65.2 K 7.7 p 20.8
30
gt Example Contamination 30
each sample consists of 70 correct assigned
particles 30 background
background (2GeV/c) e 5.8 m 0.5
p 65.2 K 7.7 p 20.8
31
gt Results (2GeV/c)

• contamination until 15 shows no big effect on
  PID
• up to 70 PID is not that bad
• step from 15 to 20 not understood yet

32
gt Results (10GeV/c)

• contamination until 15 shows no big effect on
  PID
• up to 70 PID is not that bad
• step from 15 to 20 not understood yet
• for 10 GeV/c comparable results

33
gt Test Beam Data 2007

• some problems in Test Beam Data
• clearly identified pions show very high electron
  probability
• -gt overlapping particle
• tracks
• using some cuts, the cleaning of samples is
  possible, but
• bad statistics or
• bias on electron and pion samples
• -gt no reasonable PID calculation possible

e-
p-
34
gt Test Beam Data NNs

• neural networks were trained with test beam data
• cuts were used to reduce rate of overlapping
  tracks
• anyway bad results for pion efficiency due to
  contamination with overlapping tracks
• what happens if networks from test beam were
  applied to really clean data?
• -gt apply networks on data from simulations

35
gt The Testing Sample

• data from simulations
• data that was used in AliRoot to train the
  reference neural networks
• about 20k events per particle type and momentum
• calibration
• by hand using dE/dx distributions and output of
  neural networks
• same calibration factor of 0.5 on dE/dx worked
  for all momenta (only for 1 GeV/c a higher factor
  0.8 was needed -gt attachment due to gas leak)

36
gt Results of Test Beam Networks using
Simulated Data

• results fit well to the results from former test
  beam times
• for 1 and 2 GeV/c pion efficiency lt 0.2

37
gt Summary

• we have two powerful PID methods for the TRD
• 2-dim likelihood
• PID with artificial neural networks
• both PID methods fulfill design goal and were
  tested in simulations (both) and with test
  beam data (NNs only)
• study of getting reference data from displaced
  vertices shows that very pure samples of
  electrons can be extracted
  (for momenta lt 3 GeV/c)
• contamination study shows that a contamination up
  to 15 in the reference data has nearly no effect
  on PID performance
• networks trained with 2007 test beam data show
  nice performance on simulated data

38
Thank you!
39
Backup
40
gt dE/dx in the TRD
41
gt Pion Efficiency in Simulations
42
gt Outlook

• study with large statistics simulations of g
  conversions with high pT
• test K0 and L decays for extraction of pions and
  protons, think about possibilities of kaon
  identification
• framework for extraction of reference data is in
  progress