MethodsMethodsfor fast reconstruction of for fast reconstruction of

eventsevents

Ivan KiselIvan Kisel

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

FutureDAQ Workshop, MünchenMarch 25-26, 2004

KIPKIP

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 22

CBM TriggerCBM Trigger

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 33

AlgorithmsAlgorithms

SimpleLocalParallelFast

HT - Hough TransformHT - Hough TransformCA - Cellular AutomatonCA - Cellular AutomatonEN - Elastic NetEN - Elastic NetKF - Kalman FilterKF - Kalman Filter

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 44

CA – Game “Life”CA – Game “Life”

Each cell has 8 neighboring cells, 4 adjacent orthogonally, 4 adjacent diagonally. The rules are: Survivals. Every counter with 2 or 3 neighboring counters survives for the next generation. Deaths. Each counter with 4 or more neighbors dies from overpopulation. Every counter with 1 neighbor or

none dies from isolation. Births. Each empty cell adjacent to exactly 3 neighbors is a birth cell.It is important to understand that all births and deaths occur simultaneously.

Each cell has 8 neighboring cells, 4 adjacent orthogonally, 4 adjacent diagonally. The rules are: Survivals. Every counter with 2 or 3 neighboring counters survives for the next generation. Deaths. Each counter with 4 or more neighbors dies from overpopulation. Every counter with 1 neighbor or

none dies from isolation. Births. Each empty cell adjacent to exactly 3 neighbors is a birth cell.It is important to understand that all births and deaths occur simultaneously.

Sci. Amer., 223 (1970) 120

TRACKING !TRACKING !TRACKING !TRACKING !

NOISE !NOISE !NOISE !NOISE !

TRACK !TRACK !TRACK !TRACK !

no convergence !no convergence !

RECOTRACK

RECOTRACK

RECOTRACK

RECOTRACK

RECOTRACK

RECOTRACK

GHOSTTRACK ?

GHOSTTRACK ?

movesmoves movesmoves

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 55

CA – Segment TrackingCA – Segment Tracking

NIM A387 (1997) 433; NIM A489 (2002) 389; NIM A490 (2002) 546

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 66

BTeV L1 Vertex TriggerBTeV L1 Vertex Trigger

segment trackers(~500 FPGAs)

Merge

Trigger decision to Global Level 1

Switch: sort by crossing number

track/vertex farm(~2500 DSPs)

30 station pixel detector

Find beginning and ending segments of tracks from hit clusters in 3 adjacent stations (triplets): beginning segments: required to originate from beam region ending segments: required to project out of pixel detector volume

FPGA Segment Finder (Pattern Recognition)

Match beginning and ending segments found by FPGA segment finder to form complete tracks Reconstruct primary interaction vertices using complete tracks Find tracks that are “detached” from reconstructed primaries

DSP Tracking and Vertexing

>50% of total L1 processing time is taken up by the segment matching

However

TripletTriplet

FPGAFPGA

Beauty-2002; BTeV Coll. Meeting, March 2003

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 77

LHCb L1 Vertex TriggerLHCb L1 Vertex Trigger

Z vtx histogram X,Y vtx

2d tracks in a 45o sector:

TripletTriplet

time (ms)

E

ven

ts

17 ms17 ms

CPU (CA)CPU (CA)

5 ms5 ms 15 15 ss

130 s

E

ven

ts

time (s)

FPGA (CA)FPGA (CA)

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)

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CBM Trigger AlgorithmCBM Trigger Algorithm

M1M1

M2M2

S1S1

S2S2

S3S3

S4S4

S5S5

DD

J/J/

RICHRICH

TRD, ECALTRD, ECAL

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EN – Traveling Salesman ProblemEN – Traveling Salesman ProblemContinuous ENContinuous EN

Discrete ENDiscrete EN

File Cities Extra path Time, sec(*)File Cities Extra path Time, sec(*)

(*) Pentium II/100 MHz(*) Pentium II/100 MHz

Nature, 326 (1987) 689; J. Comp. Meth. Sci. Eng., 2 (2002) 111

25-26 March 2004, München25-26 March 2004, München Ivan Kisel, KIP, Uni-HeidelbergIvan Kisel, KIP, Uni-Heidelberg 1010

EN – Ring SearchEN – Ring Search

CHEP’01, Beijing (2001) 162

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Kalman Filter –> Kalman SmootherKalman Filter –> Kalman Smoother

Kalman Filter One Processing Unit Consecutively hit by hit Kalman Filter One Processing Unit Consecutively hit by hit

Kalman Smoother Many Processing Units All hits in parallelKalman Smoother Many Processing Units All hits in parallel

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

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ConclusionConclusion

SimpleSimpleLocalLocalParallelParallelFastFast

Hough TransformHough TransformCellular AutomatonCellular AutomatonElastic NetElastic NetKalman FilterKalman Filter