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KIPKIP
TRACKING IN MAGNETIC FIELDTRACKING IN MAGNETIC FIELDBASED ON THEBASED ON THECELLULAR AUTOMATON METHODCELLULAR AUTOMATON METHOD
Ivan KiselKIP, Uni-Heidelberg
Collaboration Meeting of theCBM Experiment at the Future Accelerator Facility in DarmstadtJuly 7 - 8, 2003
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 2
KIPKIP
Straight line Parabola
SIMULATED DATA:YZ (non-bending) / XZ (bending)
SIMULATED DATA:YZ (non-bending) / XZ (bending)
TRACK MODEL:
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 3
KIPKIP
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 PROGRAMRECONSTRUCTION PROGRAM
Main ProgramMain Program
Event LoopEvent Loop
Reconstruction PartReconstruction Part
Performance PartPerformance Part
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 4
KIPKIPCELLULAR AUTOMATON METHODCELLULAR 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. .
012345
Define : •CELLS CELLS
•NEIGHBORS NEIGHBORS •RULES RULES
•EVOLUTIONEVOLUTION
Define : •CELLS CELLS
•NEIGHBORS NEIGHBORS •RULES RULES
•EVOLUTIONEVOLUTION
Create segments
Collect tracks
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 5
KIPKIPTRACK CATEGORIESTRACK CATEGORIES
RECONSTRUCTED TRACK ?RECONSTRUCTED TRACK ?
ALL MC TRACKSALL 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
noise
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 6
KIPKIPTRACKING EFFICIENCYTRACKING EFFICIENCY
PER EVENT STATISTICSMC Refset : 486MC Extras : 195ALL SIMULATED : 681
RC Refset : 459RC Extras : 144ghosts : 34clones : 1ALL RECO : 642
Refset efficiency : 0.9444Allset efficiency : 0.8855Extra efficiency : 0.7385clone probability : 0.0016ghost probability : 0.0530
RECO STATISTICS 100 events Refprim efficiency : 0.9632 | 45597Refset efficiency : 0.9279 | 48183Allset efficiency : 0.8787 | 63257Extra efficiency : 0.7512 | 15074Clone probability : 0.0012 | 78Ghost probability : 0.0660 | 4178MC tracks/event found : 632
PER EVENT STATISTICSMC Refset : 486MC Extras : 195ALL SIMULATED : 681
RC Refset : 459RC Extras : 144ghosts : 34clones : 1ALL RECO : 642
Refset efficiency : 0.9444Allset efficiency : 0.8855Extra efficiency : 0.7385clone probability : 0.0016ghost probability : 0.0530
RECO STATISTICS 100 events Refprim efficiency : 0.9632 | 45597Refset efficiency : 0.9279 | 48183Allset efficiency : 0.8787 | 63257Extra efficiency : 0.7512 | 15074Clone probability : 0.0012 | 78Ghost probability : 0.0660 | 4178MC tracks/event found : 632
RECO STATISTICS 100 events Refprim efficiency : 0.9836 | 46562Refset efficiency : 0.9485 | 49250Allset efficiency : 0.9009 | 64860Extra efficiency : 0.7779 | 15610Clone probability : 0.0011 | 74Ghost probability : 0.0518 | 3358MC tracks/event found : 648
RECO STATISTICS 100 events Refprim efficiency : 0.9836 | 46562Refset efficiency : 0.9485 | 49250Allset efficiency : 0.9009 | 64860Extra efficiency : 0.7779 | 15610Clone probability : 0.0011 | 74Ghost probability : 0.0518 | 3358MC tracks/event found : 648
100% 70%?
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 7
KIPKIPMOMENTUM ESTIMATIONMOMENTUM ESTIMATION
Least Square Fit + multiple scattering ?
Kalman Filter Fit
Least Square Fit + multiple scattering ?
Kalman Filter Fit
TRACK FIT (under development)TRACK FIT (under development)
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 8
KIPKIPTIMINGTIMING
RECONSTRUCTION STEPSRECONSTRUCTION STEPS TIMING (ms)TIMING (ms)
Fetch ROOT MC data 63.3
Copy to local arrays and sort 12.4
Create and link segments 115.7115.7
Create track candidates 53.553.5
Select tracks 2.62.6
TOTAL 248.2248.2
Off-lineFeature
30%
FPGACo-processor
68%
CPU1%
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 9
KIPKIPTrigger Performance
time (ms)
E
ven
ts
17
17
ms
ms
Mean: 15 15 ss
Max: ~130 s
CPUCPU4.8 ms4.8 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 !=> we are already within 1 ms !
•Cellular Automaton algorithm•FPGA co-processor at 50 MHz•8 processing units running in parallel
=> 15 => 15 s !s !
FPGA FPGA co-processorco-processor
E
ven
ts
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-processorFPGA co-processor
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 10
KIPKIPCOMPUTER FARM IN HEIDELBERG(32 dual PCs)
COMPUTER FARM IN HEIDELBERG(32 dual PCs)
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 11
KIPKIPTRIGGER ARCHITECTURE SIMULATIONPTOLEMY SIMULATION PACKAGE
3D TORUS 6x6x8 (275 PCs)3D TORUS 6x6x8 (275 PCs)
TRIGGER ARCHITECTURE SIMULATIONPTOLEMY SIMULATION PACKAGE
3D TORUS 6x6x8 (275 PCs)3D TORUS 6x6x8 (275 PCs)
July 7-8, 2003 Ivan Kisel - Tracking in Magnetic Field based on the Cellular Automaton Method 12
KIPKIP
• 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
PLANPLAN