Standalone FLES Package for Event Reconstruction and Selection in CBM

1. Standalone FLES Package for Event Reconstruction and Selection in CBM I. Kisel1,2, I. Kulakov1, M. Zyzak1 (for the CBM Collaboration) • Johann Wolfgang Goethe-Universität Frankfurt am Main • GSI Helmholtzzentrum für Schwerionenforschung GmbH DPG -2012 Mainz, 21 March 2012

2. Outline • Motivation • Block diagram of the First Level Event Selection (FLES) package • Reconstruction stages: • Track reconstruction • Track fit • Particle reconstruction • Summary and plans Igor Kulakov,Mainz, DPG-2012

3. Tracking Challenge in CBM Intel CPU 8 cores • 1000 charged particles/collision • Double-sided strip detectors (85% fake space points) • Non-homogeneous magnetic field • 107 AuAu collisions/sec • Track reconstruction in STS/MVD and displaced vertex search are required in the first level trigger Simulation Reconstruction CBMFLES will be based on full event reconstruction and has to be sophisticated, fast and efficient. Igor Kulakov,Mainz, DPG-2012

4. Standalone FLES Package FLES Geometry Info Hits MC CA Track Finder KF Track Fitter KFParticle Histograms Efficiencies ASCII Files Selection Quality Check ROOT • Efficient • Optimized (time) • SIMD-ized • Parallelized Standalone package for FLES has been developed. Igor Kulakov,Mainz, DPG-2012

5. Cellular Automaton Based Track Finder 1000 Tracks Track finding: Which hits in detector belong to the same track? – Cellular Automaton (CA) CA illustration: Application to straight tracks reconstruction 0. Hits (CBM) 0. Hits 1. Segments Cellular Automaton: Build short track segments. Connect according to the track model, estimate a possible position on a track. Tree structures appear, collect segments into track candidates. Select the best track candidates. 1000 Hits 2. Counters 2 4 3 1 3. Track Candidates 4. Tracks (CBM) • Cellular Automaton: • local w.r.t. data • intrinsically parallel • extremely simple • very fast • Perfect for many-core CPU/GPU ! 4. Tracks Igor Kulakov,Mainz, DPG-2012

6. CA Track Reconstruction Quality Reconstructable track: ≥ 4 consecutive MC points All set: p ≥ 0.1 GeV/c Fast set: p ≥ 1 GeV/c Ghost: purity < 70% AuAu 25 AGeV mbias; 8 STS, 0o & 8o strips; 1000 UrQMD events; Intel X5550@2.27 GHz CA Track Finder shows 98% efficiency for signal tracks Igor Kulakov,Mainz, DPG-2012

7. Kalman Filter Based Track Fit Track fit: Optimal estimation of the track parameters according to hits – Kalman Filter (KF) 3 Correction Detectorlayers Hits 1 Initializing p (r, C) r – Track parameters C – Precision Precision State vector Position, direction and momentum 2 Prediction r = { x, y, tx, ty, q/p } KF Block-diagram 1 3 2 Kalman Filter: Start with an arbitrary initialization. Add one hit after another. Improve the state vector. Get the optimal parameters after the last hit. KF as a recursive least squares method Igor Kulakov,Mainz, DPG-2012

8. Track Fit Quality pulls residuals Time: ~0.3 ms/event AuAu 25 AGeV mbias; 8 STS, 0o & 8o strips; 1000 UrQMD events; Intel X5550@2.27 GHz Track fit quality is high. Momentum resolution is 1%. Igor Kulakov,Mainz, DPG-2012

9. KFParticle for Particle Reconstruction Particle state vector Position, momentum and energy r = { x, y, z, px, py, pz,E } Functionality of the package: • Construction of the particles from tracks or another particles • Decay chainsreconstruction • 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 distanceto point, vertex or another particle • Calculation of the deviationfrom point, vertex or another particle Concept: • Mother and daughter particles have same state vector and are treated in the same way • Geometry independent • Kalman filter based Igor Kulakov,Mainz, DPG-2012

10. Particle Reconstruction Strategy Tracks χ2fit – χ2 given by a track fit χ2prim – χ2 distance to a primary vertex (PV) χ2geo – χ2 given by a particle fit χ2topo – χ2 of a particle fitted to PV χ2fit criterion Selected tracks χ2primcriterion Primarytracks Secondarytracks K0s and Λ candidates χ2geocriterion Store Selected K0sandΛ check mass Selected Λ χ2topo,zvertexcriteria PrimaryΛ SecondaryΛ Σ*+ and Σ*- candidates Ξ-and Ω- candidates χ2geocriterion χ2geo ,χ2topo , zvertexcriteria Store Store SelectedΞ-and Ω- Selected Σ*+ and Σ*- Igor Kulakov,Mainz, DPG-2012

11. Particle Reconstruction Quality K0s Λ Eff = 9.2 % S/B = 2.14 Eff = 11.3 % S/B = 1.15 Time: ~4 ms/event AuAu 25 AGeV mbias; 8 STS, 0o & 8o strips; 1000 UrQMD events; Intel X5550@2.27 GHz Particle reconstruction algorithm has been tested with K0sand Λ Igor Kulakov,Mainz, DPG-2012

12. 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. Igor Kulakov,Mainz, DPG-2012

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% • K0s andΛ 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 Igor Kulakov,Mainz, DPG-2012