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Track Finding Track Finding based on a Cellular based on a Cellular
AutomatonAutomaton
Ivan KiselIvan Kisel
Kirchhoff-Institut für PhysikKirchhoff-Institut für Physik, Uni-Heidelberg, Uni-Heidelberg
Tracking Week, GSIJanuary 24-25, 2005
KIPKIP
24-25 January 2005, GSI24-25 January 2005, GSI Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 22
Level-1 Base Reconstruction SoftwareLevel-1 Base Reconstruction Software
STS DataSTS Data
CA Track FinderCA Track Finder
KF Track FitKF Track Fit
PV FinderPV Finder
KF PV GeoFitKF PV GeoFit
KF SV GeoFitKF SV GeoFit
KF SV ConstrFitKF SV ConstrFit
PerformancePerformance
Select/DiscardSelect/DiscardEventEvent
TRD DataTRD Data
CA Track FinderCA Track Finder
KF Track FitKF Track Fit
RICH DataRICH Data
EN Ring FinderEN Ring Finder
Track MergerTrack Merger
KF Track FitKF Track Fit
L1/FPGAL1/FPGA
L1/CPUL1/CPU
HLT HLT
24-25 January 2005, GSI24-25 January 2005, GSI Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 33
Cellular 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 tracklets 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 tracklets neighborhood. Usually cellular automata employ a very simple track model which leads to utmost computational simplicity and a fast algorithm. .
654321
Define : .•CELLS -> TRACKLETSCELLS -> TRACKLETS•NEIGHBORS -> TRACK MODELNEIGHBORS -> TRACK MODEL•RULES -> BEST TRACK CANDIDATERULES -> BEST TRACK CANDIDATE•EVOLUTION -> CONSECUTIVE OR PARALLELEVOLUTION -> CONSECUTIVE OR PARALLEL
Define : .•CELLS -> TRACKLETSCELLS -> TRACKLETS•NEIGHBORS -> TRACK MODELNEIGHBORS -> TRACK MODEL•RULES -> BEST TRACK CANDIDATERULES -> BEST TRACK CANDIDATE•EVOLUTION -> CONSECUTIVE OR PARALLELEVOLUTION -> CONSECUTIVE OR PARALLEL
Collect tracks
Create tracklets
24-25 January 2005, GSI24-25 January 2005, GSI Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 44
CA Track Finding in STSCA Track Finding in STS
MC Truth -> YES
PERFORMANCE•Evaluation of efficiencies•Evaluation of resolutions•Histogramming•Timing•Statistics•Event display
MC Truth -> NO
RECONSTRUCTION•Fetch MC data•Copy to local arrays and sort•Create tracklets•Link tracklets•Create track candidates•Select tracks
Main ProgramMain Program
Event LoopEvent Loop
Reconstruction PartReconstruction Part
Performance PartPerformance Part
Parabola
Straight line
24-25 January 2005, GSI24-25 January 2005, GSI Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 55
CA Track Finding Efficiency in STS and TRDCA Track Finding Efficiency in STS and TRD
ALL MC TRACKSALL MC TRACKSRECONSTRUCTABLE TRACKS
Number of hits >= 3
REFERENCE TRACKS
Momentum > 1 GeV
24-25 January 2005, GSI24-25 January 2005, GSI Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 66
CA Track Finding – Future PlansCA Track Finding – Future Plans
•Modify according to the STS and TRD design choices•Improve the track model•Investigate efficiency of D0 secondary tracks•Increase speed