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Tracking in the CBM experiment

KIP. CBM. Tracking in the CBM experiment. I. Kisel Kirchhoff Institute of Physics, University of Heidelberg (for the CBM Collaboration). TIME05, Zurich, Switzerland October 03-07, 2005. F acility for A ntiproton and I on R esearch (GSI, Darmstadt).

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Tracking in the CBM experiment

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  1. KIP CBM Tracking in the CBM experiment I. Kisel Kirchhoff Institute of Physics, University of Heidelberg (for the CBM Collaboration) TIME05, Zurich, Switzerland October 03-07, 2005

  2. Facility for Antiproton and Ion Research (GSI, Darmstadt) • Future accelerator complex FAIR at GSI, Darmstadt: • Research program includes: • Radioactive Ion beams: Structure of nuclei far from stability • Anti-proton beams: hadron spectroscopy, anti hydrogen • Ion and laser induced plasmas: High energy density in matter • High-energy nuclear collisions:Strongly interacting matter at high baryon densities SIS 100 Tm SIS 300 Tm U: 35 AGeV p: 90 GeV Compressed Baryonic Matter (CBM) Experiment Ivan Kisel, KIP, Uni-Heidelberg

  3. Facility for Antiproton and Ion Research Photomontage of the existing and the planned research facility at GSI/FAIR. Ivan Kisel, KIP, Uni-Heidelberg

  4. Tracking Nuclear Collisions Open charm measurement: one of the prime interests of CBM, and one of the most difficult tasks • Tracking challenge: • 107 Au+Au reactions/sec • ~ 1000 charged particles/event • momentum measurement with resolution < 1% • secondary vertex reconstruction ( 30 m) • high speed data acquisition and trigger system Ivan Kisel, KIP, Uni-Heidelberg

  5. The Compressed Baryonic Matter (CBM) Experiment • Tracking, momentum measurement, vertex reconstruction: Radiation hard silicon pixel/strip detectors in a magnetic dipole field • Electron ID: RICH & TRD (& ECAL) • Hadron ID: TOF (& RICH) • Photons, p0, m: ECAL • High interaction rates ECAL (12 m) RICH magnet beam target TOF (10 m) STS (5, 10, 20, 40, 60, 80, 100 cm) TRDs (4,6, 8 m) Ivan Kisel, KIP, Uni-Heidelberg

  6. Modular Structure of DAQ RU RU RU RU RU RU RU RU RU RU RU RU RU RU RU RU MAPS, STS RICH TRD ECAL Detector 50 kB/ev 107 ev/s MAPS STS RICH TRD ECAL SFn Dt SFn Dt SFn Dt SFn Dt SFn Dt 100 ev/slice Event Builder Network N x M Scheduler SFn Dt MAPS STS RICH TRD ECAL SFn available Farm Control System 5 MB/slice Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm 105sl/s PC Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Sub-Farm Ivan Kisel, KIP, Uni-Heidelberg

  7. Cellular Automaton method for track finding • Implementations: • ARES (NIM A329, 1993) • NEMO (NIM A387, 1997) • HERA-B (NIM A489, 2002; NIM A490, 2002) • LHCb (LHCb note 2003-064, 2003) • CBM (CHEP04, 2004) • (see http://www-linux.gsi.de/~ikisel/reco/ ) • Generate a set of tracklets (similar to seeding). Tracklets are created everywhere – all chambers are seeding chambers. The same set of cuts can be applied as in the Kalman Filter track finder – the cuts reflect geometrical acceptance of a detector that is common for all methods. As hits are sorted, tracklets are generated in groups with the same leftmost hit (due to inserted loops over chambers). Therefore, every hit stores two pointers – to the first and last tracklets of his group. Every tracklet has a counter meaning possible position on a track (initially 0). • Extrapolate tracklets back to the previous layer. Usually tracklets are created (as in KF) starting from the downstream chambers and moving to the target. Therefore, during generation of the next portion of tracklets (one or two chambers closer to the target) the algorithm applies the track model to the tracklets with a common point (simple selection of the tracklets using the stored pointers, see 1). • Find neighbors and increase the counter.If neighbors (possible track continuations) are found, a counter of a current tracklet is incremented with respect to a neighbor with the largest counter. • Continue to 1 for all chambers. • Collect track candidates. Start with tracklets having the largest counter (max_counter), for each of them take a neighbor (at the right) which has a counter=max_counter-1, continue similar to the Simple Kalman Filter but follow counters (!), make branches, but no empty layers, keep the best (chi2) track for each initial tracklet with the largest counter. • Apply competition between track-candidates. After step 5 a set of track-candidates of the same length is created, therefore chi2 is well suitable criterion to sort them. After sorting start with the best (chi2) track and flag all hits of the track as used. Continue with the next track (with lower chi2), check if number of used hits is less than X (parameter, depends on track density) and flag his hits as used or delete the track. Proceed with the next track-candidate etc. • Continue to step 5, but collect tracks starting with the counter = max_counter-1.Proceed 5-7 decrementing max_counter until the shortest tracks (usually of length=tracklet_length+1) are collected. • Merge clones if necessary. In case of significant detector inefficiency merge short tracks into long tracks. • Kill ghost. Apply additional cuts to kill ghost tracks, most of them are short tracks. Drawing analogy to the Kalman method one can consider steps 1-4 as Filter, 5-7 as Smoother, and 8-9 as Cleaner. Ivan Kisel, KIP, Uni-Heidelberg

  8. Performance of track finding ALL MC TRACKS RECONSTRUCTABLE TRACKS Number of hits >= 4 REFERENCE TRACKS Momentum > 1 GeV S. Gorbunov, I. Kisel and I. Vassiliev,Analysis of D0 meson detection in Au+Au collisions at 25 AGeV, CBM-PHYS-note-2005-001 Ivan Kisel, KIP, Uni-Heidelberg

  9. Tracking in non-homogeneous magnetic field • The precision of extrapolation does not depend on a shape of the magnetic field. • One can cut off the higher-order terms in the series. S. Gorbunov and I. Kisel,An analytic formula for track extrapolation in an inhomogeneous magnetic field, CBM-SOFT-note-2005-001 Ivan Kisel, KIP, Uni-Heidelberg

  10. Elastic net for the traveling salesman problem Discrete EN (*) Pentium IV/2.4 GHz R. Durbin and D. Willshaw, An analogue approach to the travelling salesman problem, Nature, 326 (1987) 689 Ivan Kisel, KIP, Uni-Heidelberg

  11. Standalone elastic net ring finder in RICH All set: N hits ≥ 5 Ref set: N hits ≥ 15 Extra set: 5 ≤ N hits < 15 Reconstructed: ≥ 70% hits from the same MC Clone: MC reconstructed few times Ghost: < 70% hits from the same MC S. Gorbunov and I. Kisel, Elastic net for standalone RICH ring finding, CBM-SOFT-note-2005-002 Ivan Kisel, KIP, Uni-Heidelberg

  12. Summary • High track density at high rate • Most crucial blocks of the (off-line) reconstruction code ready • Work on detector optimization • CBM notes and other publications on reconstruction at: • http://www-linux.gsi.de/~ikisel/reco/ • Participants from the CBM experiment: • Walter Müller, Johann Heuser, Iouri Vassiliev and Ivan Kisel Ivan Kisel, KIP, Uni-Heidelberg

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