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Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM)

Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM). Ivan Kisel Kirchhoff-Institut für Physik , Uni-Heidelberg. KIP. Second FutureDAQ Workshop, GSI September 9, 2004. Cellular Automaton Method. Collect tracks. Create tracklets.

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Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM)

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  1. Cellular Automaton Methodfor Track Finding(HERA-B, LHCb, CBM) Ivan Kisel Kirchhoff-Institut für Physik, Uni-Heidelberg KIP Second FutureDAQ Workshop, GSI September 9, 2004

  2. Cellular Automaton Method Collect tracks Create tracklets • Define : . • CELLS -> TRACKLETS • NEIGHBORS -> TRACK MODEL • RULES -> BEST TRACK CANDIDATE • EVOLUTION -> CONSECUTIVE OR PARALLEL 1 0 5 2 3 4 Ivan Kisel, KIP, Uni-Heidelberg

  3. HERA-B Tracking NIM A489 (2002) 389; NIM A490 (2002) 546 Ivan Kisel, KIP, Uni-Heidelberg

  4. HERA-B Vertex Detector Tracking NIM A489 (2002) 389; NIM A490 (2002) 546 Ivan Kisel, KIP, Uni-Heidelberg

  5. HERA-B Pattern Tracking NIM A489 (2002) 389; NIM A490 (2002) 546 Ivan Kisel, KIP, Uni-Heidelberg

  6. HERA-B Pattern Tracking (cont.) NIM A489 (2002) 389; NIM A490 (2002) 546 Ivan Kisel, KIP, Uni-Heidelberg

  7. LHCb L1 Track Finding Triplet R-Z view • Find VELO 2D tracks (~70) and reconstruct 3D primary vertex • Reconstruct high-impact parameter tracks (~10%) in 3D • Extrapolate to TT through small magnetic field -> PT • Match tracks to L0 muon objects -> PT and PID • Select B-events using impact parameter and PT information • Use T1-3 data to improve further selection (5-10% of events) Phi-Z view • Select 2D tracks with large IP parameter • Reconstruct track by track • Start with long (best) tracks • Work in Phi-Z projection (not really 3D -> faster, but problem of displaced tracks) • Keep best candidate • Remove used phi clusters LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064 Ivan Kisel, KIP, Uni-Heidelberg

  8. LHCb L1 Tracking Efficiency 2D % 3D <70> <8> Track subsets 97.7 99.1 96.6 98.7 97.0 93.6 81.1 4.5 6.3 95.1 97.1 93.3 93.9 92.3 87.5 70.2 4.0 9.3 Reference B long Reference prim. long Reference B Reference primary Reference set All set Extra set Clone Ghost CPU (CA) FPGA (CA)  Events  Events 15 ms 5 ms  time (ms)  time (ms) LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064 Ivan Kisel, KIP, Uni-Heidelberg

  9. CBM Track Finding MC Truth -> NO • RECONSTRUCTION • Fetch MC data • Copy to local arrays and sort • Create tracklets • Link tracklets • Create track candidates • Select tracks Parabola Main Program Event Loop Reconstruction Part Performance Part MC Truth -> YES Straight line • PERFORMANCE • Evaluation of efficiencies • Evaluation of resolutions • Histogramming • Timing • Statistics • Event display Ivan Kisel, KIP, Uni-Heidelberg

  10. CBM Tracking Efficiency ALL MC TRACKS RECONSTRUCTABLE TRACKS Number of hits >= 3 REFERENCE TRACKS Momentum > 1 GeV RECO STATISTICS100 events Refprim efficiency : 98.36 | 46562 Refset efficiency : 94.85| 49250 Allset efficiency : 90.09 | 64860 Extra efficiency : 77.79 | 15610 Clone probability: 0.11| 74 Ghost probability : 5.18| 3358 Reco MC tracks/event: 648 Timing/event : 175 ms CA – INTRINSICALLY LOCAL AND PARALLEL TIMING (ms) Fetch ROOT MC data 63.3 Copy to local arrays and sort 12.4 Create and link tracklets 115.7 Create track candidates53.5 Select tracks2.6 FPGA Co-processor 98% CPU 2% Ivan Kisel, KIP, Uni-Heidelberg

  11. Status and Plan • All software is (almost) ready and tested in the CBM framework: • Track finding and fitting • Primary and secondary vertex finding and fitting (geo. and constr.) • Performance evaluation • Level-1 trigger classes • Need 1-2 weeks to finish “off-line” version • Start “on-line” version development Ivan Kisel, KIP, Uni-Heidelberg

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