KIP TRACKING IN MAGNETIC FIELD BASED ON THE CELLULAR AUTOMATON METHOD 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

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method2 Straight lineParabola SIMULATED DATA: YZ (non-bending) / XZ (bending) SIMULATED DATA: YZ (non-bending) / XZ (bending) TRACK MODEL:

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method3 MC Truth -> YES PERFORMANCE Evaluation of efficiencies Evaluation of resolutions Histogramming Timing Statistics Event display MC Truth -> NO RECONSTRUCTION Fetch ROOT MC data Copy to local arrays and sort Create segments Link segments Create track candidates Select tracks RECONSTRUCTION PROGRAM Main Program Event Loop Reconstruction Part Performance Part

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method4 CELLULAR AUTOMATON METHOD  Being essentially local and parallel cellular automata avoid exhaustive combinatorial searches, even when implemented on conventional computers..  Since cellular automata operate with highly structured information (for instance sets of track segments connecting space points), the amount of data to be processed in the course of the track search is significantly reduced..  Further reduction of information to be processed is achieved by smart definition of the segment neighborhood.  Usually cellular automata employ a very simple track model which leads to utmost computational simplicity and a fast algorithm Define : CELLSCELLS NEIGHBORSNEIGHBORS RULESRULES EVOLUTIONEVOLUTION Define : CELLSCELLS NEIGHBORSNEIGHBORS RULESRULES EVOLUTIONEVOLUTION Create segmentsCollect tracks

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method5 TRACK CATEGORIES RECONSTRUCTED TRACK ? ALL MC TRACKS RECONSTRUCTABLE TRACKS Number of hits >= 3 REFERENCE TRACKS Momentum > 1 GeV 70% 100% % OF CORRECT HITS FITTING ACCURACY % OF CORRECT HITS FITTING ACCURACY nois e

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method6 TRACKING EFFICIENCY PER EVENT STATISTICSMC Refset : 486MC Extras : 195ALL SIMULA TED : 681 RC Refset : 459 RC Extras : 144 ghosts : 34 clones : 1 ALL RECO : 642 Refset efficiency : Allset efficiency : Extra efficiency : clone probability : ghost probability : RECO STATISTICS 100 events Refprim efficiency : | Refset efficiency : | Allset efficiency : | Extra efficiency : | Clone probability : | 78 Ghost probability : | 4178 MC tracks/event found : 632 PER EVENT STATISTICSMC Refset : 486MC Extras : 195ALL SIMULA TED : 681 RC Refset : 459 RC Extras : 144 ghosts : 34 clones : 1 ALL RECO : 642 Refset efficiency : Allset efficiency : Extra efficiency : clone probability : ghost probability : RECO STATISTICS 100 events Refprim efficiency : | Refset efficiency : | Allset efficiency : | Extra efficiency : | Clone probability : | 78 Ghost probability : | 4178 MC tracks/event found : 632 RECO STATISTICS 100 events Refprim efficiency : | Refset efficiency : | Allset efficiency : | Extra efficiency : | Clone probability : | 7 4 Ghost probability : | 3358 MC tracks/event found : 6 48 RECO STATISTICS 100 events Refprim efficiency : | Refset efficiency : | Allset efficiency : | Extra efficiency : | Clone probability : | 7 4 Ghost probability : | 3358 MC tracks/event found : %70% ?

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method7 MOMENTUM ESTIMATION Least Square Fit + multiple scattering ? Kalman Filter Fit Least Square Fit + multiple scattering ? Kalman Filter Fit TRACK FIT (under development)

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method8 TIMING RECONSTRUCTION STEPS TIMING (ms) Fetch ROOT MC data63.3 Copy to local arrays and sort12.4 Create and link segments115.7 Create track candidates53.5 Select tracks2.6 TOTAL248.2 Off-line Feature 30% FPGA Co-processor 68% CPU 1%

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method9 Trigger Performance  time (ms)  Events 17 ms 15  s Mean: 15  s Max: ~130  s CPU 4.8 ms 1) Tracking efficiency 97—99% 2) PV resolution 46  m 3) Timing 4.8 ms Expect a factor 7—8 in CPU power in 2007 (PASTA report) => we are already within 1 ms ! Cellular Automaton algorithm FPGA co-processor at 50 MHz 8 processing units running in parallel => 15  s ! FPGA co-processor  Events  time (  s) SIMILAR TASK (LHCb experiment, CERN) K.Giapoutzis, “LHCb Vertex Trigger Algorithmus”, Diploma Thesis, 2002 SIMILAR TASK (LHCb experiment, CERN) K.Giapoutzis, “LHCb Vertex Trigger Algorithmus”, Diploma Thesis, 2002 FPGA co-processor

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method10 COMPUTER FARM IN HEIDELBERG (32 dual PCs) COMPUTER FARM IN HEIDELBERG (32 dual PCs)

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method11 TRIGGER ARCHITECTURE SIMULATION PTOLEMY SIMULATION PACKAGE 3D TORUS 6x6x8 (275 PCs) TRIGGER ARCHITECTURE SIMULATION PTOLEMY SIMULATION PACKAGE 3D TORUS 6x6x8 (275 PCs)

KIP July 7-8, 2003Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method12 Track search with digitized detector Track fit including multiple scattering FPGA adapted algorithm Development of a trigger architecture Build a trigger prototype Track search with digitized detector Track fit including multiple scattering FPGA adapted algorithm Development of a trigger architecture Build a trigger prototype PLAN