Roberto Barbera (Alberto Pulvirenti)University of Catania and INFN

ACAT 2003 – Tsukuba – 01-05.12.2003

Combined tracking in the

ALICE detector

Outline1. Introduction2. The neural network

model3. Standalone tracking4. “Combined” tracking5. Summary and outlook

The CERN Large Hadron Collider

ALICE

3 millions of volumes in the simulation!

The ALICE program: search for QGP

Pb+Pb @ LHC (5.5 A TeV)

The

Big

Ban

g

The

Littl

e B

ang

The ALICE tracking problem1/100 of a Pb+Pb @ LHC !

Simulation and reconstruction of a “full” (central) Pb+Pb collision at LHC (about 84000 primary tracks!) takes about 15 hours on a top-PC and produces an total output bigger than 2 GB.

Motivations1. Stand alone tracking in ITS only.

“high-rate acquisition” runs: HOW: only the fast ALICE detectors turned ON (ITS, Muon-

Arm, TRD, …) WHY: combined analysis of specific QGP signatures REQUIREMENT: good performance for high transv.

momentum (pt >1 GeV/c )2. “Combined” tracking.

recovering particles which go into the TPC dead zones

recovering particles which decay in the TPC barrel and for which it is not possible to determine a suitable seed for the Kalman Filter algorithm

The ALICE Inner Tracking System (ITS)

6 layers (2 SPD, 2 SDD, 2 SSD)Rmin ~ 4 cm ; Rmax ~ 44 cm ; L ~ 98 cm

2198 modules ; >12.5·106 read-out channels

Data: ITS fully reconstructed space pointsNeurons: oriented segments between recpoint pairs

Implementation: neurons

Implementation: weightsFinal target: obtaining poly-lines with one point for each ITS layer

Relations between “connected” segs

sequences•guess for track segments•good alignment requested

crossings•need to be “resolved”•constant weight

nhkijhkij Aw or or sin1 Bw jkjh

CutsCriteria used to choose which pairs have to be connected to form a “neuron”:

1.Space points only on adjacent layers.2.Cut on the polar angle difference between

neurons (layer by layer)3.Cut on the curvature of the circle passing

through the estimated primary vertex and the two points of the pair (layer by layer)

4.“Helix matching cut”

max

j

Vj

i

Viij a

zzazz

…where a is the length of the circle arc going from

the vertex projection in the xy plane to each point of

the pair.

Work-flow“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 azimuthal sectors

RISK: edge effectsthe tracks crossing a sector boundary will not be recognizable by the ANN tracker. Found negligible for Pt > 1 GeV/c

ITS sectioning

~ 180 s fora “full” eventon a 1 Ghz PC

Ingredients of the simulations• Parameterized HIJING generator in 0 < < 180 for three

multiplicities: ~80 events at “full” multiplicity (84210 primaries) ~80 events at “half” multiplicity (42105 primaries = 84210 / 2) 100 events at “quarter” multiplicity (21053 primaries =

84210 / 4)• B = 0.2 T and primary vertex at (0, 0, 0)• Full slow reconstruction in ITS and TPC• (for combined) ITS tracking V1• SAME CUTS & NEURAL NETWORK PARAMS FOR ALL

TESTS• Subdivision of ITS barrel into 20 azimutal sectors • Evaluation criteria:

“Good” track at least 5 correct points Otherwise it is labeled as “fake”

“Findable” track: generated track containing at least 5 ITS recpoints

“Efficiency” = # “good” / # “findables”

Stand alone: efficiency for “quarter” events

Stand alone: efficiency for “half” events

Stand alone: efficiency for “full” events

Summary table

M/Mmax

Efficiency

(%)

Fake prob.(%)

¼ 88.8 ± 0.8 1.45 ± 0.07

½ 86.4 ± 0.6 3.38 ± 0.09

1 79.0 ± 0.4 9.33 ± 0.11

Particles with transverse momentum > 1 GeV/c

“Combined” tracking work-flow and defs•Operations:

Standard TPC + ITS KF tracking Removing “used” space points Performing neural tracking only on remaining

space points•Tracking efficiency for Kalman and

Kalman + neural Efficiency = “good” / “findables” “findable” = a track with at least 5 ITS

recpoints (EVEN IF IT IS NOT FINDABLE IN TPC)

“good” = found track with at least 5 correct pointsOtherwise it is labeled as “fake”

“Combined” : efficiency for “quarter” events

Kalman only

Kalman + neural

“Combined” : efficiency for “half” events

Kalman only

Kalman + neural

“Combined” : efficiency for “full” events

Kalman only

Kalman + neural

Summary table

All K Fake (all)

M/Mmax

KF Comb

KF Comb KF Comb

KF Comb

¼ 81.6 +12.3 83.3 +11 71.2 +20.

2 0.96 +1.44

½ 79.7 +10.3 81.2 +9.3 70.6 +16.

8 2.31 +2.16

1 73.8 +8.2 75.3 +7.6 64.5 +11.9 4.91 +4.4

7

Particles with transverse momentum > 1 GeV/c

Summary and outlook• Stand-alone ITS tracking has an efficiency

of almost 80% for the highest multipilicity events for high transverse momentum tracks (Pt > 1 GeV/c)

• “Combined” tracking increases by ~8-12% the tracking efficiency in the high transverse momentum range (Pt > 1 GeV /c), and gives an large contribution for the Kaon reconstruction efficiency (+12-20%)

• What’s next: address the very difficult problem of ITS stand-alone tracking of low momentum particles (Pt < 1 GeV/c). Multi-combined trackings and genetic algorithms presently under consideratio