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Track Reconstruction Algorithms for the ALICE High-Level Trigger

Track Reconstruction Algorithms for the ALICE High-Level Trigger. ALICE HLT team: T.Alt, C.Loizides, G.Overbekk, M.Richter, D.Rohrich, A.Vestbo, T.Vik and ALICE Core Offline group: C.Cheshkov, J.Belikov, P.Hristov & M.Ivanov 13-17 Feb 2006 CHEP’2006. Outline. Introduction

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Track Reconstruction Algorithms for the ALICE High-Level Trigger

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  1. Track Reconstruction Algorithmsfor the ALICE High-Level Trigger ALICE HLT team: T.Alt, C.Loizides, G.Overbekk, M.Richter, D.Rohrich, A.Vestbo, T.Vik and ALICE Core Offline group: C.Cheshkov, J.Belikov, P.Hristov & M.Ivanov 13-17 Feb 2006 CHEP’2006

  2. Outline • Introduction • ALICE High Level Trigger (HLT) • Physics cases • Tracking algorithms for ALICE TPC • Fast Hough Transform tracking for TPC • Tracking for ALICE ITS • Example of triggers • D0K trigger • High-Pt jet trigger • Conclusions Track Reconstruction Algorithms for the ALICE HLT

  3. ALICE High Level Trigger Detectors Detectors Detectors Detectors 12GB/s 12GB/s 12GB/s 12GB/s DAQ DAQ DAQ DAQ HLT HLT HLT HLT 1.2GB/s 1.2GB/s 1.2GB/s 1.2GB/s Mass Storage Mass Storage • Data rate from central PbPb collisions (dN/dy~2000-4000): 200Hz*(30Mb-60Mb)=6-12Gb/s • Max mass storage bandwidth ~1.2Gb/s • The goal of HLT is to reduce the data rate without biasing important physics information: • Event triggering • “Regions of Interest” • Advanced data compression • Requirements: • Fast and robust online reconstruction • Sufficient tracking efficiency and resolution • Fast analysis of important physics observables Track Reconstruction Algorithms for the ALICE HLT

  4. ALICE HLT - Physics Cases • Large computer cluster (about 400 nodes) • Off-the-shell PCs connected with high-bandwidth network • Fault-tolerant publisher/subscriber principle • FPGA co-processors for local pattern recognition • “Barrel” HLT Physics cases: • Jets • Aim: trigger for high-Et jets • Requires: TPC tracking (+ITS) • Open charm • Aim: trigger for D0K • Requires: TPC and ITS tracking • Charmonium spectroscopy • Aim: trigger for dielectrons • Requires: TPC and TRD tracking, TRD electron PID • Pile-up removal in p-p • Aim: reduce the size of TPC raw data by filtering out background events • Requires: TPC tracking Track Reconstruction Algorithms for the ALICE HLT

  5. ALICE TPC • Acceptance ||<0.9 • 18 trapezoidal sectors • 72 Cathode pad readout chambers • 159 rows ~5.6x105 pads E E 84 cm 250 cm B=0.5T Only primary tracks with Pt>1GeV/c are shown 500 cm Readout chambers ~15-30% occupancy ~50 million ADC amplitudes ~3 million clusters ~10000 tracks in acceptance ~50 Mbytes compressed data Track Reconstruction Algorithms for the ALICE HLT

  6. ALICE HLT algorithms for TPC tracking • Low multiplicity (up to dN/dy~2000-3000): • Cluster finder + track follower (in Conformal Mapping space) • ~13s for dN/dy=4000 (including 4s for cluster finder) • Cluster finder implemented on FPGA • High multiplicity (up to dN/dy~8000): • Standard ‘grayscale’ Hough Transform • Satisfactory tracking efficiency • But… • High fake track rate • Resolution affected by the high multiplicity environment • Poor time performance: 1000-2000s for central PbPb event • Fast ‘counting’ Hough Transform approach Track Reconstruction Algorithms for the ALICE HLT

  7. Hough Transform: Highly parallelizable – FPGA implementation Computing time - massive random memory access Efficiency and resolution limitations – parameter space binning Tracking algorithm: Consider only primary tracks Neglect energy losses and multiple scattering  track model: helix crossing the origin Split TPC data in bins of pseudo-rapidity  3D2D Hough Transform Parameter space – histogram with tracks helix parameters Space-points transformed into curves corresponding to all possible track helices they can belong to Parameter space peaks are found and tracks are reconstructed Hough Transform TPC tracking Image space – TPC sector Parameter space Track curvature Emission angle Track Reconstruction Algorithms for the ALICE HLT

  8. ‘Grayscale’ HT: Parameter space bins incremented by raw ADC counts (accumulate charge along particle trajectory) Peaks: charge>threshold ‘Counting’ HT: Parameter space bins incremented by distance to last filled pad-row (count the # of ‘gaps’ along particle trajectory) Peaks: #gaps<threshold Hough Transform TPC tracking TPC sector • Powerful identification of good track candidates • 100% intrinsic TPC efficiency  Good tracks have ‘almost’ no gaps • Unbiased extraction of track parameters • Background does not affect the parameter space peaks • Large room for speeding up • Perform HT for “cluster” edges and fill the entire “cluster” at once • Early fake tracks removal by accumulated # of gaps Track Reconstruction Algorithms for the ALICE HLT

  9. Parameter Space Definition TPC sector layout • Conformal Mapping space (x,y)  =x/(x2+y2) , =y/(x2+y2) • Define two curves =const. (circles) • Tracks are represented by two points on these curves 1 and 2 • Space-points are transformed into straight lines in parameter space  Linear Hough transform •  curves chosen at middle and outer sector edge  Min correlation between variables  Powerful seeding of track candidates (by ordered processing of pad-rows ) Conformal space Track Reconstruction Algorithms for the ALICE HLT

  10. Hough transform tracking • Other performance improvements: • Reduced parameter space size - 2 bytes/bin • Extensive usage of LUTs • Dynamic pointers between neighbor track candidates fast “jumping” during the parameter space filling • Fast parameterized calculation of pseudo-rapidity index • Example of tracking in one TPC sector: • Track candidates are identified by a simple peak finder Track Reconstruction Algorithms for the ALICE HLT

  11. Tracking Performance Efficiency Resolution • Tracking efficiency  95% • No dependence on multiplicity • Sources of inefficiencies: • -binning • Overlaps in parameter space • Mult.scat. + energy losses • Pt resolution dominated by param. space bin size: (1/Pt)~bin size  Pt/Pt=(Ahough*Pt + Bmult.scat) • No dependence on multiplicity Track Reconstruction Algorithms for the ALICE HLT

  12. Overall computing time for Hough Transform tracking • For comparison: Computing time ~ time needed just to unpack Huffman encoded TPC data • Only ~5% of the time is outside param. space filling Track Reconstruction Algorithms for the ALICE HLT

  13. Inner Tracking System • Silicon Pixel Detectors (2D) • 80+160 ladders • ~107 channels • Silicon Drift Detectors (2D) • 14+24 ladders • ~1.4x105 channels • Silicon Strip Detectors (1D) • 34+38 ladders • ~2.5x106 channels R=43.6 cm Vertex reconstruction (primary, secondary) resolution <100 μm L=97.6 cm Track Reconstruction Algorithms for the ALICE HLT

  14. ITS tracking for HLT • Offline ITS clusterer • Optimized for time performance offline Z vertex finder: • Based on SPD clusters only • Simple histogramming method • Simplified and optimized for time performance offline tracking algorithm: • No cluster error parametrization • Reduced tree of hypothesis in combinatorial Kalman filter • (Silicon Drift Layers not used) Track Reconstruction Algorithms for the ALICE HLT

  15. ITS tracking performance Efficiency Impact param resolution • Quite satisfactory overall efficiency • ITS tracking almost completely removes “ghost” Hough tracks dN/dy=4000 • Impact parameter resolution dominated by SPD (~ off-line resolution) • For 1 GeV/c track: 60 microns (trans) and 160 microns (long) Track Reconstruction Algorithms for the ALICE HLT

  16. HLT ITS Timings • The numbers in brackets are without using the 2 SDD layers Track Reconstruction Algorithms for the ALICE HLT

  17. D0->K trigger • Invariant mass resolution ~35 MeV/c2 (about 2x-3x offline one) • Efficiency and selectivity of the trigger is under investigation • The expected rejection factor is ~10-30 M=(355)MeV/c2 • Time performance (starting from reconstructed tracks): Track Reconstruction Algorithms for the ALICE HLT

  18. High-Pt Jet Trigger (PhD Thesis, C.Loizides) Reconstructed jet energy (fraction) Jet energy resolution Ideal case Tracking The losses due to HLT tracking are negligible compared to fluctuations in “missing” neutral part of the jets and “background” in PbPb Track Reconstruction Algorithms for the ALICE HLT

  19. Conclusions • Fast Hough-Transform TPC Tracking: • Very good efficiency (stable up to dN/dy~8000) • Pt resolution worsens linearly with Pt • ~5s comp. time for central PbPb event with dN/dy~4000 ~8 Mbytes/s processing rate (compressed data) ~0.15 s/ADC count (hit) • FPGA implementation is under development - would allow to diminish the computing time to hundreds of milliseconds • ITS Tracking: • Hough Transform tracks are efficiently propagated to ITS • Fast and efficient ITS cluster finder, vertex and tracking • Track parameters resolution is greatly improved (excellent impact parameter resolution) • High-Pt jet and open charm triggers look very promising • Further development of the HLT algorithms and functionality is underway Be ready for first LHC beams in 2007 ! Track Reconstruction Algorithms for the ALICE HLT

  20. SPARES Track Reconstruction Algorithms for the ALICE HLT

  21. Tracking Performance • The presented tracking performance obtained with the following Hough space parameters: • Binning: 80(1)x120(2)x100() ~2x pad size in  direction • Range: tracking with minimum Pt = 0.5GeV/c • Chosen Hough space is a compromise between tracking efficiency, resolution and required computing time • Resolution ~ bin size • Comp. time ~ 1/bin size • Comp. time ~ 1/Ptmin Track Reconstruction Algorithms for the ALICE HLT

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