KIP TRACKING IN MAGNETIC FIELD BASED ON THE CELLULAR AUTOMATON METHOD TRACKING IN MAGNETIC FIELD...

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:


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


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


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


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




100% 70%?


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)


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%


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


KIPKIPCOMPUTER FARM IN HEIDELBERG(32 dual PCs)

COMPUTER FARM IN HEIDELBERG(32 dual PCs)


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)


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

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KIP TRACKING IN MAGNETIC FIELD BASED ON THE CELLULAR AUTOMATON METHOD TRACKING IN MAGNETIC FIELD...

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