Neural tracking in

ALICEAlberto Pulvirenti – University and I.N.F.N. of Alberto Pulvirenti – University and I.N.F.N. of

CataniaCatania

ACAT ’02 conferenceACAT ’02 conference

Moscow, June 26 2002Moscow, June 26 2002

OutlineOutline

The ALICE experimentThe ALICE experiment Tracking in ALICETracking in ALICE Why an ITS stand-alone tracking?Why an ITS stand-alone tracking? ImplementationImplementation ResultsResults Work in progress and outlookWork in progress and outlook

The Large Hadron The Large Hadron ColliderCollider

http://www.cern.ch

~9 km

LHC

SPS

CERN

The 4 LHC experimentsThe 4 LHC experiments

ALICE’s objective: QGP ALICE’s objective: QGP studystudy

Pb+Pb @ LHC (5.5 A TeV)

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ang

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ALICE track multiplicityALICE track multiplicityA sketch…

ALICE track multiplicityALICE track multiplicity

Simulation and reconstruction of a “full” (central) Pb+Pb collision at LHC (about 84000 primary tracks!) takes about 24 hours of a top-PC and produces an output bigger than 2 GB.

A sketch… of 1/100 of a typical ALICE event

The ALICE detectorThe ALICE detector

Tracking in ALICETracking in ALICE Time Projection Chamber.Time Projection Chamber.

~180 points per track ~180 points per track main contribution. main contribution. Inner Tracking System.Inner Tracking System.

6 points close to primary vertex 6 points close to primary vertex improves resolution near improves resolution near to the production vertex.to the production vertex.

Standard procedure:Standard procedure:1.1. Points in the TPC outermost pad-rows are arranged into Points in the TPC outermost pad-rows are arranged into

suitable track seeds.suitable track seeds.2.2. the seeds are propagated through the TPC towards its the seeds are propagated through the TPC towards its

innermost pad-row, according to a Kalman filter algorithm innermost pad-row, according to a Kalman filter algorithm for both recognition and reconstruction.for both recognition and reconstruction.

3.3. each track found in the TPC is propagated in the ITS and its each track found in the TPC is propagated in the ITS and its parameters are refined with the aid of the six best matched parameters are refined with the aid of the six best matched ITS points.ITS points.

Why an ITS stand-alone Why an ITS stand-alone tracking?tracking?…… because the TPC is a “slow” detectorbecause the TPC is a “slow” detector

some events could be produced in asome events could be produced in a “high-rate acquisition “high-rate acquisition mode”mode”, by turning on only the fastest ALICE modules (ITS, , by turning on only the fastest ALICE modules (ITS, Muon Spectrometer), to produce large amounts of data useful Muon Spectrometer), to produce large amounts of data useful for all analyses needing high statistics.for all analyses needing high statistics.

in this case, we need at least a satisfactory efficiency for high in this case, we need at least a satisfactory efficiency for high transverse momentum (transverse momentum (pptt >1 G >1 GeVeV//cc).).

…… because some particles decay within the TPC because some particles decay within the TPC barrel volume, and the standard TPC tracking barrel volume, and the standard TPC tracking doesn’t manage to create seeds for them.doesn’t manage to create seeds for them.

in this case, the tracking is performed in this case, the tracking is performed afterafter completing completing the standard Kalman procedure, and working the standard Kalman procedure, and working only on the only on the points which the Kalman method didn’t usepoints which the Kalman method didn’t use..

Implementation: Implementation: 1 – 1 – definitionsdefinitions

Neuron: oriented track segment 2 indexes: [sij]links two consecutive points in the particle’s path according to a well-defined direction

Implementation: Implementation: 1 – 1 – definitionsdefinitions

Implementation: Implementation: 1 – 1 – definitionsdefinitions

Weight: geometrical relations between neurons 4 idxs: [wijkl]Geometrical constraint: only neurons which share a point have a non zero weight

Implementation: Implementation: 1 – 1 – definitionsdefinitions

Weight: geometrical relations between neurons 4 idxs: [wijkl]Geometrical constraint: only neurons which share a point have a non zero weightCase 1: sequence

• guess for a track segment • good alignment requested

Implementation:Implementation: 1 – 1 – definitionsdefinitions

Weight: geometrical relations between neurons 4 idxs: [wijkl]Geometrical constraint: only neurons which share a point have a non zero weightCase 1: sequence

• guess for a track segment, • good alignment requested

Case 2: crossing• negative weight• leads to a competition

between units

Weight: geometrical relations between neurons 4 idxs: [wijkl]Geometrical constraint: only neurons which share a point have a non zero weightCase 1: sequence

• guess for a track segment, • good alignment requested

Case 2: crossing• negative weight• leads to a competition

between units

Implementation: Implementation: 1 – 1 – definitionsdefinitions

Neural Network Neural Network Simulation SpecificsSimulation Specifics Associative memory topology Associative memory topology

(single layer of fully connected units).(single layer of fully connected units). Real valued (“sigmoidal”) activation function, Real valued (“sigmoidal”) activation function,

limited between 0 and 1.limited between 0 and 1. Random initialization.Random initialization. Asynchronous updating cycle (one unit at a time).Asynchronous updating cycle (one unit at a time). Stabilization threshold on the average activation Stabilization threshold on the average activation

variation after a complete updating cycle.variation after a complete updating cycle. Resolution of competitions to the advantage of the Resolution of competitions to the advantage of the

unit with the greatest real activation.unit with the greatest real activation. Binary mapping of “on” and “off” units with a Binary mapping of “on” and “off” units with a

threshold of 0.6 on the final real neural activation.threshold of 0.6 on the final real neural activation.

Implementation: Implementation: 2 – cuts2 – cuts

Needed to limit the number of point pairs used to create neurons

1. Check only couples on adjacent layers

2. Cut on the difference in polar angle ()

3. Cut on the curvature of the projected circle passing through the two points and the calculated vertex

4. “Helix matching cut”

…where a is the corresponding circle arc of

the projection in the xy plane

Implementation: Implementation: 3 – 3 – procedureprocedure“Step by step” procedure

(removing the points used at the end of each step) Many curvature cut steps, with increasing cut value Sectioning of the ITS barrel into N azymuthal sectors

RISK: edge effectsthe tracks crossing a sector boundary will not be recognizable by the ANN tracker

Implementation: 4 – Implementation: 4 – reconstructionreconstruction

Track reconstruction: Kalman Filter. Track reconstruction: Kalman Filter. (ref.: A. Badalà et al., NIM A(2002) in press and references therein)(ref.: A. Badalà et al., NIM A(2002) in press and references therein)..

““vertex constrained” seed.vertex constrained” seed. A helix is estimated by using the two

outermost points and the experimental vertex (the same which is used for neuron creation cut).

two operational phases:two operational phases:1.1. vertex vertex layer 6. layer 6.

2.2. layer 6 layer 6 vertex. vertex.

Test trial ingredientsTest trial ingredients Test on a simulation produced with the HIJING event generator Test on a simulation produced with the HIJING event generator

interface (developed within the AliRoot framework), and tracks interface (developed within the AliRoot framework), and tracks transported through the detector by GEANT 3.21:transported through the detector by GEANT 3.21: All detectors and all physical effects turned “on”.All detectors and all physical effects turned “on”. Fully detailed geometry, simulation and reconstruction in the ITS.Fully detailed geometry, simulation and reconstruction in the ITS. ALICE “default” number of primary tracks ALICE “default” number of primary tracks

(84210 in the pseudorapidity region |(84210 in the pseudorapidity region || < 8.0).| < 8.0).

Track definition for efficiency evaluationTrack definition for efficiency evaluation

CriterionCriterion GOOD TRACK (fake GOOD TRACK (fake otherwise)otherwise) FINDABLE TRACKFINDABLE TRACK

““SOFT”SOFT” at least 5 right pointsat least 5 right points Has at least 5 points in Has at least 5 points in ITSITS

““HARD”HARD” all 6 point must be correctall 6 point must be correct Has a point for each Has a point for each layerlayer

Efficiency = # good tracks (fake tracks) / # Efficiency = # good tracks (fake tracks) / # findable tracksfindable tracks

““Signal-to-noise ratio”Signal-to-noise ratio”Layer 1 2 3 4 5 6 Aver

age

Good / All 46%

60%

65%

69%

77%

74%

65%

Unused good / All unused

21%

37%

45%

51%

68%

63%

47%

Stand-alone tracking: Stand-alone tracking: results (I)results (I)

Number of found tracks, efficiency and CPU time as a function of the # of sectors.Only one event analyzed.

Test choice: 18 sectors

CPU time: ~10% of the time requested the whole ITS at once

PC used: PIII 1 GHz

SOFT

good

fake

Stand-alone tracking: Stand-alone tracking: results (II)results (II)

Stand-alone tracking: Stand-alone tracking: results (III)results (III)

Dip angle () resolution (in mrad)

sigma = 3.69 0.01

Azimuthal angle () resolution (in mrad)

sigma = 4.71 0.01 pt resolution (in % of true value)

sigma = 13.4 0.3 %

(only 6 points!)

Stand-alone tracking: Stand-alone tracking: results (III)results (III)

Transverse impact parameter resolution (in microns)

sigma = 79.7 0.1

Longitudinal impact parameter resolution (in microns)

sigma = 265.6 0.4

Stand-alone tracking Stand-alone tracking results (III)results (III)Parameters resolution

Neural Kalman (without vertex. constr.)

pt (%) 13.4 0.3 1.57 0.02

(mrad) 4.71 0.01 1.40 0.08

(mrad)

3.69 0.01 1.60 0.08

Dt (m) 79.7 0.1 ~ 50

Dz(m) 265.6 0.4 ~150Efficiency for tracks with pt 1 GeV / c

Efficiency (%) Fake (%)

Neural “soft” 78.2 3.0 9.9 0.9

Kalman “soft” 72.8 2.9 4.9 0.6

““Combined” tracking: Combined” tracking: results (III)results (III)

Results for Pt 1 GeV / c:

KalmanKalman

Efficiency:Efficiency:72.8

2.9

Fake prob.:Fake prob.: 4.9 0.6

Efficiency per particle [pt 1 GeV/c]

K

Kalman 74.7 2.7 64.5 0.8

““Combined” tracking: Combined” tracking: results (III)results (III)

The “findable” tracks are counted among all ITS findable tracks (even the ones which are

NOT findable in the TPC)

Results for Pt 1 GeV / c:

KalmanKalman CombinedCombined

Efficiency:Efficiency:72.8

2.983.0 3.0

Fake prob.:Fake prob.: 4.9 0.6 7.0 0.7

Efficiency per particle [pt 1 GeV/c]

K

Kalman 74.7 2.7 64.5 0.8

Combined 84.7 2.9 76.2 0.8

10% increase!

Conclusions & work in Conclusions & work in progressprogress

The Neural Network tracking algorithm has been successfully The Neural Network tracking algorithm has been successfully adapted to the unprecedented ALICE multiplicityadapted to the unprecedented ALICE multiplicity

Implementation has been done in the official AliRoot off-line Implementation has been done in the official AliRoot off-line framework based on ROOT.framework based on ROOT.

Recognition efficiency is comparable with the Kalman Filter one, Recognition efficiency is comparable with the Kalman Filter one, in the range of pin the range of ptt > 1 GeV/c. > 1 GeV/c.

Under study:Under study: Improving the neural algorithm performances for LOW transverse Improving the neural algorithm performances for LOW transverse

momentum tracks [ pmomentum tracks [ ptt < 0.2 GeV/c ] ( < 0.2 GeV/c ] (not a trivial task!not a trivial task!).). Alternative possible techniques for the same purpose (adapting Alternative possible techniques for the same purpose (adapting

some existing algorithms like elastic tracking, elastic arms some existing algorithms like elastic tracking, elastic arms algorithm, or developing a genetic algorithm).algorithm, or developing a genetic algorithm).

Future developments (for “combined” tracking).Future developments (for “combined” tracking). Improving track parameter resolution by including also the Improving track parameter resolution by including also the

TPC/TRDTPC/TRD points “unused” by Kalman tracking. points “unused” by Kalman tracking.