Combined tracking in the ALICE detector

1. Combined tracking in the ALICE detector Roberto Barbera (Alberto Pulvirenti) University of Catania and INFN ACAT 2003 – Tsukuba – 01-05.12.2003

2. Outline • Introduction • The neural network model • Standalone tracking • “Combined” tracking • Summary and outlook

3. The CERN Large Hadron Collider

4. 3 millions of volumes in the simulation! ALICE

5. The ALICE program: search for QGP Pb+Pb @ LHC (5.5 A TeV) The Little Bang The Big Bang

6. The ALICE tracking problem 1/100 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.

7. Motivations • 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 ) • “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

8. 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

9. Implementation: neurons Data:ITS fully reconstructed space points Neurons:orientedsegments between recpoint pairs

10. Implementation: weights Final target:obtaining poly-lines with one point for each ITS layer Relations between “connected” segs • crossings • need to be “resolved” • constant weight • sequences • guess for track segments • good alignment requested

11. Cuts Criteria used to choose which pairs have to be connected to form a “neuron”: • Space points only on adjacent layers. • Cut on the polar angle difference between neurons (layer by layer) • Cut on the curvature of the circle passing through the estimated primary vertex and the two points of the pair (layer by layer) • “Helix matching cut” …where a is the length of the circle arc going from the vertex projection in the xy plane to each point of the pair.

12. 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 effects the tracks crossing a sector boundary will not be recognizable by the ANN tracker. Found negligible for Pt > 1 GeV/c

13. ITS sectioning ~ 180 s for a “full” event on a 1 Ghz PC

14. 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”

15. Stand alone: efficiency for “quarter” events

16. Stand alone: efficiency for “half” events

17. Stand alone: efficiency for “full” events

18. Summary table Particles with transverse momentum > 1 GeV/c

19. “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 points • Otherwise it is labeled as “fake”

20. “Combined” : efficiency for “quarter” events Kalman + neural Kalman only

21. “Combined” : efficiency for “half” events Kalman + neural Kalman only

22. “Combined” : efficiency for “full” events Kalman + neural Kalman only

23. Summary table Particles with transverse momentum > 1 GeV/c

24. 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