1. Standalone FLES Package for Event Reconstruction and Selection in CBM DPG -2012 Mainz, 21 March 2012 I. Kisel 1,2, I. Kulakov 1, M. Zyzak 1 (for the CBM Collaboration) 1.Johann Wolfgang Goethe-Universität Frankfurt am Main 2.GSI Helmholtzzentrum für Schwerionenforschung GmbH

2. 21.03.2012Igor Kulakov, Mainz, DPG-20122/13Outline  Motivation  Block diagram of the First Level Event Selection (FLES) package  Reconstruction stages:  Track reconstruction  Track fit  Particle reconstruction  Summary and plans

3. 21.03.2012Igor Kulakov, Mainz, DPG-20123/13 Tracking Challenge in CBM Simulation Reconstruction Simulation Reconstruction Intel CPU 8 cores CBM FLES will be based on full event reconstruction and has to be sophisticated, fast and efficient. 1000 charged particles/collision Double-sided strip detectors (85% fake space points) Non-homogeneous magnetic field 10 7 AuAu collisions/sec Track reconstruction in STS/MVD and displaced vertex search are required in the first level trigger

4. 21.03.2012Igor Kulakov, Mainz, DPG-20124/13 Standalone FLES Package Standalone package for FLES has been developed. Efficient Optimized (time) SIMD-ized Parallelized CA Track Finder KF Track Fitter KFParticle Selection Quality Check FLES FLES Hits Geometry Info ROOT Efficiencies ASCII Files Histograms MC

5. 21.03.2012Igor Kulakov, Mainz, DPG-20125/13 Cellular Automaton Based Track Finder Track finding: Which hits in detector belong to the same track? – Cellular Automaton (CA) Cellular Automaton: local w.r.t. data intrinsically parallel extremely simple very fast Perfect for many-core CPU/GPU ! 0. Hits (CBM) 1000 Hits 4. Tracks (CBM) 1000 Tracks Cellular Automaton: 1.Build short track segments. 2.Connect according to the track model, estimate a possible position on a track. 3.Tree structures appear, collect segments into track candidates. 4.Select the best track candidates. 0. Hits 1. Segments 2 3 4 2. Counters 3. Track Candidates 4. Tracks 1 CA illustration: Application to straight tracks reconstruction

6. 21.03.2012Igor Kulakov, Mainz, DPG-20126/13 CA Track Reconstruction Quality Efficiency and ratios, % Fast Prim Set97.7 All Set88.9 Clone0.1 Ghost0.3 Reco Tracks/ev121 Time/ev, ms8.2 Reconstructable track: ≥ 4 consecutive MC points All set: p ≥ 0.1 GeV/c Fast set: p ≥ 1 GeV/c Ghost: purity < 70% CA Track Finder shows 98% efficiency for signal tracks AuAu 25 AGeV mbias; 8 STS, 0 o & 8 o strips; 1000 UrQMD events; Intel X5550@2.27 GHz

7. 21.03.2012Igor Kulakov, Mainz, DPG-20127/13 Kalman Filter Based Track Fit Track fit: Optimal estimation of the track parameters according to hits – Kalman Filter (KF) Detector layers Hits  (r, C) r – Track parameters C – Precision Initializing Prediction Correction Precision 1 2 3 r = { x, y, t x, t y, q/p } Position, direction and momentum State vector Kalman Filter: 1. Start with an arbitrary initialization. 2. Add one hit after another. 3. Improve the state vector. 4. Get the optimal parameters after the last hit. KF as a recursive least squares method KF Block-diagram 1 2 3

8. 21.03.2012Igor Kulakov, Mainz, DPG-20128/13 pulls residuals Track Fit Quality Time: ~0.3 ms/event AuAu 25 AGeV mbias; 8 STS, 0 o & 8 o strips; 1000 UrQMD events; Intel X5550@2.27 GHz residualspulls x, μ my, μ m t x, 10 -3 t y, 10 -3 p, % xytxtx tyty q/p 9.7920.480.771.070.7 1.1 1.3 Track fit quality is high. Momentum resolution is 1%.

9. 21.03.2012Igor Kulakov, Mainz, DPG-20129/13 KFParticle for Particle Reconstruction Concept: Mother and daughter particles have same state vector and are treated in the same way Geometry independent Kalman filter based r = { x, y, z, p x, p y, p z, E } Position, momentum and energy Particle state vector Functionality of the package: Construction of the particles from tracks or another particles Decay chains reconstruction Transport of the particles (on the distance, to a point, to another particle, to vertex) Simple access to the particle parameters and their errors Calculation of the distance to point, vertex or another particle Calculation of the deviation from point, vertex or another particle

10. 21.03.2012Igor Kulakov, Mainz, DPG-201210/13 Particle Reconstruction Strategy χ 2 fit – χ 2 given by a track fit χ 2 prim – χ 2 distance to a primary vertex (PV) χ 2 geo – χ 2 given by a particle fit χ 2 topo – χ 2 of a particle fitted to PV Tracks χ 2 fit criterion Secondary tracks χ 2 prim criterion Selected K 0 s and Λ Store Selected tracks χ 2 geo criterion check mass Secondary Λ Primary Λ χ 2 topo, z vertex criteria Selected Λ Primary tracks K 0 s and Λ candidates Σ *+ and Σ *- candidates Selected Σ *+ and Σ *- χ 2 geo, χ 2 topo, z vertex criteria Store Ξ - and Ω - candidates Selected Ξ - and Ω - χ 2 geo criterion Store

11. 21.03.2012Igor Kulakov, Mainz, DPG-201211/13 Particle Reconstruction Quality Time: ~4 ms/event AuAu 25 AGeV mbias; 8 STS, 0 o & 8 o strips; 1000 UrQMD events; Intel X5550@2.27 GHz Particle reconstruction algorithm has been tested with K 0 s and Λ Eff = 11.3 % S/B = 1.15 Eff = 9.2 % S/B = 2.14 K 0 s Λ

12. 21.03.2012Igor Kulakov, Mainz, DPG-201212/13 Scalability on Many-core System Given n threads each filled with 1000 events, run them on specific n logical cores, 1 thread per 1 core. The FLES package shows strong scalability on many-core systems.

13. 21.03.2012Igor Kulakov, Mainz, DPG-201213/13 Summary & Plans  The first version of the standalone FLES package has been developed and tested  Signal tracks reconstruction efficiency is 98%  Tracks momentum resolution is 1%  K 0 s and Λ reconstruction efficiencies are 11% and 9% with signal to background ratios 1.2 and 2.1  Linear scalability on many-core systems  Throughput of 1700 minimum bias events per second on 80-core system Plans:  Further optimization with respect to time  Full event topology reconstruction