Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM)

Cellular Automaton MethodCellular Automaton Methodfor Track Findingfor Track Finding

(HERA-B, LHCb, CBM)(HERA-B, LHCb, CBM)

Ivan KiselIvan Kisel

Kirchhoff-Institut für PhysikKirchhoff-Institut für Physik, Uni-Heidelberg, Uni-Heidelberg

Second FutureDAQ Workshop, GSISeptember 9, 2004

KIPKIP

9 September 2004, GSI9 September 2004, GSI Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 22

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. .

543210

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


HERA-B TrackingHERA-B Tracking

NIM A489 (2002) 389; NIM A490 (2002) 546


HERA-B Vertex Detector TrackingHERA-B Vertex Detector Tracking

NIM A489 (2002) 389; NIM A490 (2002) 546


HERA-B Pattern TrackingHERA-B Pattern Tracking

NIM A489 (2002) 389; NIM A490 (2002) 546


HERA-B Pattern Tracking (cont.)HERA-B Pattern Tracking (cont.)

NIM A489 (2002) 389; NIM A490 (2002) 546


LHCb L1 Track FindingLHCb L1 Track Finding

TripletTriplet

LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064

Find VELO 2D tracks (~70) and reconstruct 3D primary vertex Reconstruct high-impact parameter tracks (~10%) in 3D Extrapolate to TT through small magnetic field -> PT Match tracks to L0 muon objects -> PT and PID Select B-events using impact parameter and PT information Use T1-3 data to improve further selection (5-10% of events)

Phi-Z viewPhi-Z view

R-Z viewR-Z view

•Select 2D tracks with large IP parameter•Reconstruct track by track•Start with long (best) tracks•Work in Phi-Z projection (not really 3D -> faster, but problem of displaced tracks)•Keep best candidate•Remove used phi clusters


LHCb L1 Tracking EfficiencyLHCb L1 Tracking Efficiency

time (ms)

E

ven

ts CPU (CA)CPU (CA)

5 ms5 ms 15 15 ss

Even

ts

time (s)

FPGA (CA)FPGA (CA)

LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064

Track subsetsTrack subsets

Reference B longReference prim. longReference BReference primaryReference setAll setExtra setCloneGhost

97.799.196.698.797.093.681.1 4.5 6.3

95.197.193.393.992.387.570.2 4.0 9.3

2D % 3D<70> <8>

Noise level, %

Number of Clusters

Accepted Clusters

Data reduction

Useful Clusters filtered

Efficiency RefB

Efficiency RefPrim

0.00 607607 524524 13.7% 3.9% 99.3% 98.9%

0.05 659659 530530 19.6% 3.9% 99.3% 98.9%

0.10 710710 536536 24.5% 3.9% 99.3% 98.9%

0.15 761761 544544 28.5% 3.6% 99.3% 98.9%

0.20 812812 553553 31.9% 3.6% 99.3% 98.9%

0.30 914914 573573 37.3% 3.5% 99.3% 98.9%


CBM Track FindingCBM Track Finding

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


CBM Tracking EfficiencyCBM Tracking Efficiency

RECO STATISTICS 100 events Refprim efficiency : 98.36 | 46562 Refset efficiency : 94.85 | 49250 Allset efficiency : 90.09 | 64860 Extra efficiency : 77.79 | 15610 Clone probability : 0.11 | 74 Ghost probability : 5.18 | 3358 Reco MC tracks/event : 648 Timing/event : 175 ms

RECO STATISTICS 100 events Refprim efficiency : 98.36 | 46562 Refset efficiency : 94.85 | 49250 Allset efficiency : 90.09 | 64860 Extra efficiency : 77.79 | 15610 Clone probability : 0.11 | 74 Ghost probability : 5.18 | 3358 Reco MC tracks/event : 648 Timing/event : 175 ms

ALL MC TRACKSALL MC TRACKSRECONSTRUCTABLE TRACKS

Number of hits >= 3

REFERENCE TRACKS

Momentum > 1 GeV

TIMING (ms)

Fetch ROOT MC data 63.3

Copy to local arrays and sort 12.4

Create and link tracklets 115.7115.7

Create track candidates 53.553.5

Select tracks 2.62.6

TIMING (ms)

Fetch ROOT MC data 63.3

Copy to local arrays and sort 12.4

Create and link tracklets 115.7115.7

Create track candidates 53.553.5

Select tracks 2.62.6

FPGACo-processor

98%

CPU2%

CA – INTRINSICALLY LOCAL AND PARALLEL

CA – INTRINSICALLY LOCAL AND PARALLEL


Status and PlanStatus and Plan

• All software is (almost) ready and tested in the CBM framework:•Track finding and fitting•Primary and secondary vertex finding and fitting (geo. and constr.)•Performance evaluation•Level-1 trigger classes

• Need 1-2 weeks to finish “off-line” version• Start “on-line” version development

Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM)

Documents

Transcript of Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM)