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Straight line

TRACKING IN MAGNETIC FIELD BASED ON THE CELLULAR AUTOMATON METHOD Ivan Kisel KIP, Uni-Heidelberg Collaboration Meeting of the CBM Experiment at the Future Accelerator Facility in Darmstadt July 7 - 8, 2003. SIMULATED DATA: YZ (non-bending) / XZ (bending). TRACK MODEL:. Straight line.

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Straight line

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  1. TRACKING IN MAGNETIC FIELDBASED ON THECELLULAR AUTOMATON METHODIvan KiselKIP, Uni-HeidelbergCollaboration Meeting of theCBM Experiment at the Future Accelerator Facility in DarmstadtJuly 7 - 8, 2003

  2. SIMULATED DATA: YZ (non-bending) / XZ (bending) TRACK MODEL: Straight line Parabola Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  3. RECONSTRUCTION PROGRAM MC Truth -> NO • RECONSTRUCTION • Fetch ROOT MC data • Copy to local arrays and sort • Create segments • Link segments • Create track candidates • Select tracks Main Program Event Loop Reconstruction Part MC Truth -> YES Performance Part • PERFORMANCE • Evaluation of efficiencies • Evaluation of resolutions • Histogramming • Timing • Statistics • Event display Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  4. Create segments Collect tracks CELLULAR AUTOMATON METHOD • Define : • CELLS • NEIGHBORS • RULES • EVOLUTION 4 5 0 3 2 1 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  5. TRACK CATEGORIES ALL MC TRACKS RECONSTRUCTABLE TRACKS Number of hits >= 3 REFERENCE TRACKS Momentum > 1 GeV • % OF CORRECT HITS • FITTING ACCURACY RECONSTRUCTED TRACK ? 100% 70% noise Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  6. TRACKING EFFICIENCY PER EVENT STATISTICS MC Refset : 486 MC Extras : 195 ALL SIMULATED : 681 RC Refset : 459 RC Extras : 144 ghosts : 34 clones : 1 ALL RECO : 642 Refset efficiency : 0.9444 Allset efficiency : 0.8855 Extra efficiency : 0.7385 clone probability : 0.0016 ghost probability : 0.0530 RECO STATISTICS100 events Refprim efficiency : 0.9632 | 45597 Refset efficiency : 0.9279 | 48183 Allset efficiency : 0.8787 | 63257 Extra efficiency : 0.7512 | 15074 Clone probability : 0.0012 | 78 Ghost probability : 0.0660 | 4178 MC tracks/event found : 632 ? 100% 70% RECO STATISTICS100 events Refprim efficiency : 0.9836 | 46562 Refset efficiency : 0.9485| 49250 Allset efficiency : 0.9009 | 64860 Extra efficiency : 0.7779 | 15610 Clone probability : 0.0011 | 74 Ghost probability : 0.0518 | 3358 MC tracks/event found : 648 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  7. MOMENTUM ESTIMATION TRACK FIT (under development) • Least Square Fit • + multiple scattering ? • Kalman Filter Fit Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  8. TIMING Off-line Feature 30% FPGA Co-processor 68% CPU 1% Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  9. FPGA co-processor Trigger Performance SIMILAR TASK (LHCb experiment, CERN) K.Giapoutzis, “LHCb Vertex Trigger Algorithmus”, Diploma Thesis, 2002 2) PV resolution 46 mm 1) Tracking efficiency 97—99% 3) Timing 4.8 ms CPU Expect a factor 7—8 in CPU power in 2007 (PASTA report) => we are already within 1 ms ! 4.8 ms  Events 17 ms FPGA co-processor  time (ms) Mean: 15 ms  Events • Cellular Automaton algorithm • FPGA co-processor at 50 MHz • 8 processing units running in parallel • => 15 ms ! Max: ~130 ms  time (ms) Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  10. COMPUTER FARM IN HEIDELBERG (32 dual PCs) Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  11. TRIGGER ARCHITECTURE SIMULATION PTOLEMY SIMULATION PACKAGE 3D TORUS 6x6x8 (275 PCs) Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

  12. PLAN • Track search with digitized detector • Track fit including multiple scattering • FPGA adapted algorithm • Development of a trigger architecture • Build a trigger prototype Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method

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