1 / 11

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.

kayo
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related