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

2. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg2Outline CBM Experiment at GSI CBM Experiment at GSI Cellular Automaton (CA) Method Cellular Automaton (CA) Method CBM Track Finding based on CA CBM Track Finding based on CA Efficiency and Timing Efficiency and Timing Our Experience (HERA-B and LHCb) Our Experience (HERA-B and LHCb) Summary Summary

3. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg3 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 10 5  Hadron identification: RPC, RICH   Measurement of photons,  0,  : electromagnetic calorimeter (ECAL)   High speed data acquisition and trigger system

4. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg4 CBM Reconstruction Algorithms M1 M2 S1 S2 S3 S4 S5 D J/  RICH TRD, ECAL   10 7 Au+Au reactions/sec with high track multiplicity (700 – 1000)   determination of displaced vertices with high resolution (  30  m)  identification of electrons and hadrons Conformal Mapping Hough Transform Cellular Automaton Conformal Mapping Hough Transform Cellular Automaton

5. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg5 Cellular Automaton Method 5 432 1 0 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 tracksCreate tracklets  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 neighborhood..  Usually cellular automata employ a very simple track model which leads to utmost computational simplicity and a fast algorithm.. 1.NIM A329 (1993) 262 2.NIM A387 (1997) 433 3.NIM A489 (2002) 389 4.NIM A490 (2002) 546

6. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg6 CBM 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 Program Event Loop Reconstruction Part Performance Part Parabola Straight line

7. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg7 CBM Cellular Automaton Tracking Efficiency RECO STATISTICS 100 events Refprim efficiency 98.36 46562 Refset efficiency 94.85 4 9250 Allset efficiency 90.09 64860 Extra efficiency 7 7.79 15610 Clone probability 0. 1 1 7 4 Ghost probability 5.18 3358 Reco MC tracks/event 6 48 Timing/ event 175 ms RECO STATISTICS 100 events Refprim efficiency 98.36 46562 Refset efficiency 94.85 4 9250 Allset efficiency 90.09 64860 Extra efficiency 7 7.79 15610 Clone probability 0. 1 1 7 4 Ghost probability 5.18 3358 Reco MC tracks/event 6 48 Timing/ event 175 ms ALL MC TRACKS RECONSTRUCTABLE TRACKS Number of hits >= 3 REFERENCE TRACKS Momentum > 1 GeV TIMING (ms) Fetch ROOT MC data 63.3Copy to local arrays and sort 12.4 115.7 Create and link tracklets 115.7 53.5 Create track candidates 53.5 2.6 Select tracks 2.6 TIMING (ms) Fetch ROOT MC data 63.3Copy to local arrays and sort 12.4 115.7 Create and link tracklets 115.7 53.5 Create track candidates 53.5 2.6 Select tracks 2.6 FPGA co-processor 98% CPU 2% CA – INTRINSICALLY LOCAL AND PARALLEL CA – INTRINSICALLY LOCAL AND PARALLEL

8. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg8 Our Experience: HERA-B Pattern Tracking NIM A489 (2002) 389; NIM A490 (2002) 546; I. Gorbounov, Ph.D. Thesis, Uni-Siegen, 2004 Cellular Automaton Kalman Filter Hough Transform RANGER CATS Time per Event, sec Accuracy Efficiency ~3x ~300 tracks/event

9. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg9  time (ms)  Events 15  s Mean: 15  s CPU (CA) 4.8 ms Mean: 4.8 ms 1. 1.Tracking efficiency 97—99% 2. 2.Primary vertex resolution 46  m 3. 3.Timing 4.8 ms Expect a factor 7—8 in CPU power in 2007 (PASTA report) => we are already within 1 ms ! Cellular Automaton algorithm FPGA co-processor at 50 MHz 8 processing units running in parallel => 15  s !  Events  time (  s) Our Experience: LHCb Level-1 Trigger FPGA (CA) LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064; K. Giapoutzis, Diploma Thesis, Uni-Heidelberg, 2002

10. 30 September 2004, CHEP 04Ivan Kisel, KIP, Uni-Heidelberg10Summary Fast and efficient track finder based on the cellular automaton method Fast and efficient track finder based on the cellular automaton method Locality suitable for inhomogeneous magnetic field Locality suitable for inhomogeneous magnetic field Possible implementation in hardware to accelerate the combinatorial part Possible implementation in hardware to accelerate the combinatorial part