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Tracking in the High Rate Environment of the HERA-B Detector

TIME 05 Zurich. Tracking in the High Rate Environment of the HERA-B Detector. A. Spiridonov. DESY Zeuthen / ITEP Moscow. HERA-B tracking detectors Tracking algorithms Performance in commissioning phase Final tracking performance Matching Tracks fitting

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Tracking in the High Rate Environment of the HERA-B Detector

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  1. TIME 05 Zurich Tracking in the High Rate Environment of the HERA-B Detector A. Spiridonov DESY Zeuthen / ITEP Moscow HERA-B tracking detectors Tracking algorithms Performance in commissioning phase Final tracking performance Matching Tracks fitting Momentum/mass resolution Summary

  2. HERA-B Detector Fixed target experiment at 920 GeV p beam at HERA Trigger Chambers (TC) Pattern Chambers (PC) Magnet Vertex Detector System (VDS) A. Spiridonov

  3. Recorded Data and Physics  HERA-B was designed to study CP violation with B-mesons  Achieved rejection factor for dilepton trigger of order 104  Dilepton trigger: 3×105 reconstructed J/Y (ee,mm modes)  Minimum bias trigger: 210×106 events acquired  Goals for physics study: B physics (cross sections); QCD studies; Charmonium spectroscopy; D physics. A. Spiridonov

  4. Track Reconstruction Challenges  Large track density  Combination of different detector technologies with complicated geometry: double sided silicon micro-strip detectors; honeycomb drift tubes (5 and 10 mm); Micro-strip gaseous chambers (MSGC-GEM).  Detector imperfections during the commissioning and first period of data taking A. Spiridonov

  5. Track Reconstruction Strategy Trigger Chambers Pattern Chambers Magnet Chambers Vertex Detector ECAL & MUON  Vertex Detector: track finding  Pattern Chambers: track finding  Magnet Chambers: track propagation  Trigger Chambers: track propagation  Matching of VDS and PC track segments  Tracks fitting A. Spiridonov

  6. Vertex Detector System of HERA-B  double-sided Si detectors  50 mm pitch  12 mm resolution  5% peak occupancy  64 detectors, 8 superlayers  150,000 channels A. Spiridonov

  7. CATS: Cellular Automation for Tracking  Track finding in VDS, NIM A489 p.389  Track finding in OTR PC (data processing), NIM A490 p.546  See also TIME05 talk: I.Kisel on cellular automation in CBM  Semi-global method using local formation of spacepoints in neighbored detector planes  Cell (spacepoint): 3D track segment inside a superlayer  Model of cellular automation for tracking can be regarded as local discrete form of the Denby-Peterson neural net  Tracking efficiency 98% for VDS on real data A. Spiridonov

  8. Main Tracker of HERA-B Outer Tracker (OTR) Honeycomb drift tubes 115,000 TDC channels 5 and 10 mm cells 0°,+5°,-5° stereo layers peak occupancy 5% Inner Tracker (ITR) Micro-Strip Gaseous Chambers (MSGS-GEM) 140,000 analog readout channels pitch 300 mm 0°,+5°,-5° stereo layers peak occupancy 30% A. Spiridonov

  9. Performance of the Main Tracker Design Data before 2001 Data after 2001 Outer Tracker Resolution 400-500mm Efficiency 85-90% 10-18% channels turned off Resolution 360mm Efficiency 94-97% 3% channels turned off Resolution 200mm Efficiency 98% Inner Tracker Resolution 80mm Efficiency 95% Resolution 150mm Efficiency 85% Resolution 150mm Efficiency 85% Drastic improvement of the Outer Tracker after 2001 A. Spiridonov

  10. Ranger: Track Following Algorithm  NIM A395, p.169  Track candidates seeding in the Pattern Tracker  Hit triplets are used as seeds  Both left/right assignments for drift distances  Propagate all branches in parallel, but reject inferior  Maximize quality estimator Q = #step - #fault - w c2  Kalman Filter A. Spiridonov

  11. Ranger: Track Propagation in the Magnet  NIM A426, p.268  Inhomogeneous magnetic field  3D concurrent track evolution  Kalman filter  4th, 5th order Runge-Kutta  Timing for 2 interactions + 1 with J/Y  Pattern Chamber track finding 0.4 s  Magnet Chamber propagation 0.2 s  Trigger Chamber propagation 0.04 s A. Spiridonov

  12. Main Tracker Reconstruction before 2001  Real event with 150 tracks  Reconstructed with magnet tracking  Tracking efficiency about 80% A. Spiridonov

  13. Tracking Efficiency before 2001  Monte Carlo: 2 minimum bias + 1 with J/Y(mm)  Stand alone reconstruction in Pattern Tracker Ranger CATS Efficiency per m track 0.97±0.02 0.94±0.02  Full event reconstruction for real data before 2001 SLT seeds Default algorithms Ranger Ranger CATS 288±23 209±17 190±17 Reconstructed J/Y(mm) 1 0.85±0.04 0.81±0.04 Efficiency per m track  More realistic simulation to obtain MC & data agreement A. Spiridonov

  14. SLT Seeding in off-line Reconstruction  Coarse tracking for dileptons by Second Level Trigger  3D track following of SLT seeds by Ranger off-line  Number of following in parallel branches can be made bigger in the case of few seeds, enhancing efficiency  Efficiency ~95% for tracks being of physical interest  Finally, SLT seeds were implemented for CATS also  Physics results (B, cC etc) were obtained before 2001 A. Spiridonov

  15. Tracking Performance after 2001  Not triggered: efficiency estimation on data using KS(pp) One p made from VDS-ECAL/RICH and OTR is searched Efficiency 95-97% for not triggered tracks on data  Efficiency 96-99% for triggered tracks  CATS Ranger 1.8 106mm triggers 1209±48 1252±44 number of J/Y(mm) S/(S+B) 0.57±0.01 0.64±0.01  Full event reconstruction about 3 s on HERA-B farm  Pattern recognition takes 20-25% only A. Spiridonov

  16. Matching of VDS and PC track segments  Pairs (x,y,tX,tY,1/p) for VDS and PC given (p not defined)  One Kalman Filter step to estimate p and c2  Use the c2 as a quality estimator Wrong match Proper match  Tail: Moliere scattering and pattern recognition fails A. Spiridonov

  17. Performance of Matching on Real Data  KS(pp) and J/Y(mm) were used to evaluate efficiency  Event number in signals and background vs matching c2 J/Y(mm) KS(pp)  For c2 < 200: 98% p and 100% m were matched  Selected pairs (ambiguities also) stored as tracks, superior flagged A. Spiridonov

  18. Track Fit in HERA-B  Use of the Kalman Filter technique  The system state vector (x,y,tX,tY,1/p)  4th, 5th order Runge-Kutta  3 filter/smoother iterations with outliers removal at last J/Y(mm) Mass 3093 MeV/c2 Width 38 MeV/c2 A. Spiridonov

  19. Track Fit Performance on Real Data  Was investigated on the sample of J/Y(mm)  The sample was divided into a 4×4 grid  Binning in muon momentum: 14, 26, 44, 72 GeV/c Relative deviations smaller then 3·10-3 A. Spiridonov

  20. Momentum Resolution on Real Data  The sample of J/Y(mm) divided into a 4×4 grid  D(m) = m/2 ( D(p1)/p1 + D(p2)/p2 ) O  Muons momenta p1, p2 4 values of D(p)/p fitted to 4×4 D(m) A. Spiridonov

  21. S U M M A R Y  Performances of VDS and OTR detectors close to designed were achieved  Tracking efficiency 98% in VDS  Tracking efficiency 98% for triggered and 96% for not triggered tracks in OTR  Matching efficiency about 98% for VDS and OTR track segments  For J/Y(mm) small relative mass bias about 10-3 and mass resolution 38 MeV/c2  Data taking completed, high statistics recorded  Physics analysis in progress A. Spiridonov

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