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. .

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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