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

3. Outline 1.Introduction 2.The neural network model 3.Standalone tracking 4.“Combined” tracking 5.Summary and outlook

4. The CERN Large Hadron Collider

5. ALICE 3 millions of volumes in the simulation!

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

7. 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.

8. Motivations 1.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

9. The ALICE Inner Tracking System (ITS) 6 layers (2 SPD, 2 SDD, 2 SSD) R min ~ 4 cm ; R max ~ 44 cm ; L ~ 98 cm 2198 modules ; >12.5·10 6 read-out channels

10. Data: ITS fully reconstructed space points Neurons: oriented segments between recpoint pairs Implementation: neurons

11. Implementation: weights Final 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

12. Cuts Criteria 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” …where a is the length of the circle arc going from the vertex projection in the xy plane to each point of the pair.

13. 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 P t > 1 GeV/c

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

15. 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”

16. Stand alone: efficiency for “quarter” events

17. Stand alone: efficiency for “half” events

18. Stand alone: efficiency for “full” events

19. Summary table M/M max Efficiency (%) Fake prob. (%) ¼ 88.8 ± 0.81.45 ± 0.07 ½ 86.4 ± 0.63.38 ± 0.09 1 79.0 ± 0.49.33 ± 0.11 Particles with transverse momentum > 1 GeV/c

20. “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”

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

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

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

24. Summary table All  KFake (all) M/M max KF Comb KF Comb KF Comb KF Comb ¼ 81.6+12.383.3+1171.2+20.20.96+1.44 ½ 79.7+10.381.2+9.370.6+16.82.31+2.16 1 73.8+8.275.3+7.664.5+11.94.91+4.47 Particles with transverse momentum > 1 GeV/c

25. Summary and outlook Stand-alone ITS tracking has an efficiency of almost 80% for the highest multipilicity events for high transverse momentum tracks (P t > 1 GeV/c) “Combined” tracking increases by ~8-12% the tracking efficiency in the high transverse momentum range (P t > 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 (P t < 1 GeV/c). Multi-combined trackings and genetic algorithms presently under consideratio