Fast reconstruction of tracks in the inner tracker of the CBM experiment

1. Fast reconstruction of tracksin the inner tracker of the CBM experiment Ivan Kisel (for the CBM Collaboration) Kirchhoff Institute of Physics University of Heidelberg, Germany CBM KIP CHEP 2004 Interlaken, Switzerland, 30.09.04

2. Outline • CBM Experiment at GSI • Cellular Automaton (CA) Method • CBM Track Finding based on CA • Efficiency and Timing • Our Experience (HERA-B and LHCb) • Summary Ivan Kisel, KIP, Uni-Heidelberg

3. Compressed Baryonic Matter (CBM) Experiment at GSI  Radiation hard Silicon pixel/strip detectors in a magnetic dipole field  Electron detectors: RICH & TRD & ECAL: pion suppression up to 105  Hadron identification: RPC, RICH • Measurement of photons, p0, h: electromagnetic calorimeter (ECAL) • High speed data acquisition and trigger system Ivan Kisel, KIP, Uni-Heidelberg

4. CBM Reconstruction Algorithms • Conformal Mapping • Hough Transform • Cellular Automaton TRD, ECAL RICH S5 S4 S3 S2 D S1 M2 M1 J/y • 107 Au+Au reactions/sec with high track multiplicity (700 – 1000) • determination of displaced vertices with high resolution ( 30 m)  identification of electrons and hadrons Ivan Kisel, KIP, Uni-Heidelberg

5. Cellular Automaton Method Collect tracks Create tracklets • Define :. • CELLS -> TRACKLETS • NEIGHBORS -> TRACK MODEL • RULES -> BEST TRACK CANDIDATE • EVOLUTION -> CONSECUTIVE OR PARALLEL • NIM A329 (1993) 262 • NIM A387 (1997) 433 • NIM A489 (2002) 389 • NIM A490 (2002) 546 1 0 5 2 3 4 Ivan Kisel, KIP, Uni-Heidelberg

6. CBM Track Finding MC Truth -> NO • RECONSTRUCTION • Fetch MC data • Copy to local arrays and sort • Create tracklets • Link tracklets • Create track candidates • Select tracks Parabola Main Program Event Loop Reconstruction Part Performance Part MC Truth -> YES Straight line • PERFORMANCE • Evaluation of efficiencies • Evaluation of resolutions • Histogramming • Timing • Statistics • Event display Ivan Kisel, KIP, Uni-Heidelberg

7. CBM Cellular Automaton Tracking Efficiency RECO STATISTICS100 events Refprim efficiency 98.36 46562 Refset efficiency 94.8549250 Allset efficiency 90.0964860 Extra efficiency 77.79 15610 Clone probability0.1174 Ghost probability 5.18 3358 Reco MC tracks/event648 Timing/event175 ms ALL MC TRACKS RECONSTRUCTABLE TRACKS Number of hits >= 3 REFERENCE TRACKS Momentum > 1 GeV CA – INTRINSICALLY LOCAL AND PARALLEL TIMING (ms) Fetch ROOT MC data 63.3 Copy to local arrays and sort 12.4 Create and link tracklets 115.7 Create track candidates53.5 Select tracks2.6 FPGA co-processor 98% CPU 2% Ivan Kisel, KIP, Uni-Heidelberg

8. Our Experience: HERA-B Pattern Tracking Hough Transform Cellular Automaton Kalman Filter Accuracy Efficiency Time per Event, sec RANGER ~3x CATS ~300 tracks/event NIM A489 (2002) 389; NIM A490 (2002) 546; I. Gorbounov, Ph.D. Thesis, Uni-Siegen, 2004 Ivan Kisel, KIP, Uni-Heidelberg

9. Our Experience: LHCb Level-1 Trigger • Tracking efficiency 97—99% • Primary vertex resolution 46 mm • Timing 4.8 ms CPU (CA)  Events Mean:4.8 ms Expect a factor 7—8 in CPU power in 2007 (PASTA report) => we are already within 1 ms ! FPGA (CA)  time (ms)  Events Mean: 15 ms • Cellular Automaton algorithm • FPGA co-processor at 50 MHz • 8 processing units running in parallel • => 15 ms !  time (ms) LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064; K. Giapoutzis, Diploma Thesis, Uni-Heidelberg,2002 Ivan Kisel, KIP, Uni-Heidelberg

10. Summary • Fast and efficient track finder based on the cellular automaton method • Locality suitable for inhomogeneous magnetic field • Possible implementation in hardware to accelerate the combinatorial part Ivan Kisel, KIP, Uni-Heidelberg