1. Cellular Automaton Method for Track Finding (HERA-B, LHCb, CBM) Ivan Kisel Kirchhoff-Institut für Physik, Uni-Heidelberg Second FutureDAQ Workshop, GSI September 9, 2004 KIP

2. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg2 Cellular 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.. 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

3. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg3 HERA-B Tracking NIM A489 (2002) 389; NIM A490 (2002) 546

4. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg4 HERA-B Vertex Detector Tracking NIM A489 (2002) 389; NIM A490 (2002) 546

5. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg5 HERA-B Pattern Tracking NIM A489 (2002) 389; NIM A490 (2002) 546

6. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg6 HERA-B Pattern Tracking (cont.) NIM A489 (2002) 389; NIM A490 (2002) 546

7. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg7 LHCb L1 Track Finding Triplet 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 view R-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

8. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg8 LHCb L1 Tracking Efficiency  time (ms)  Events CPU (CA) 5 ms 15  s  Events  time (  s) FPGA (CA) LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064 Track subsets Reference B long Reference prim. long Reference B Reference primary Reference set All set Extra set Clone Ghost 97.7 99.1 96.6 98.7 97.0 93.6 81.1 4.5 6.3 95.1 97.1 93.3 93.9 92.3 87.5 70.2 4.0 9.3 2D % 3D Noise level, % Number of Clusters Accepted Clusters Data reduction Useful Clusters filtered Efficiency RefB Efficiency RefPrim 0.0060752413.7%3.9%99.3%98.9% 0.0565953019.6%3.9%99.3%98.9% 0.1071053624.5%3.9%99.3%98.9% 0.1576154428.5%3.6%99.3%98.9% 0.2081255331.9%3.6%99.3%98.9% 0.3091457337.3%3.5%99.3%98.9%

9. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg9 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

10. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg10 CBM 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

11. 9 September 2004, GSIIvan Kisel, KIP, Uni-Heidelberg11 Status 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