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Muon Reconstruction with Moore and MuonIdentification

Muon Reconstruction with Moore and MuonIdentification. The Moore/MUID group Atlas Physics Workshop Athens, May 2003. Outline. Reconstruction method and architecture Muon spectrometer inert material treatment Single m performances H →4 m Z→2 m Test beam data reconstruction

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Muon Reconstruction with Moore and MuonIdentification

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  1. Muon Reconstruction with Moore and MuonIdentification The Moore/MUID group Atlas Physics Workshop Athens, May 2003

  2. Outline • Reconstruction method and architecture • Muon spectrometer inert material treatment • Single m performances • H→4m • Z→2m • Test beam data reconstruction • Moore and Muid as Event Filter algorithms • Conclusions

  3. Tasks • Moore (Muon Object Oriented REconstruction) • reconstruction in the MuonSpectrometer • MuonIdentification • Muon reconstruction and identification • Divided in two parts : • MuidStandAlone: • Back tracking of the MOORE tracks to the interaction point • Muid Comb: • Combination of the muon and the inner detector tracks • Both work in ATHENA (= the ATLAS reconstruction framework)

  4. MOORE Reconstruction Strategy (I) rpc barrel F projection rpc rpc Search for region of activity in the f projection and RZ projection rpc rpc rpc barrel RZ projection MDT

  5. MOORE Reconstruction Strategy(II) • Pattern recognition in the MDTs • the drift distance is calculated from the drift time, by applying various corrections on it (TOF, second coordinate, propagation along the wire, Lorenz effect). Among the 4 tangential lines the best one is found. • Track segment combination. MDT pattern recognition MDT mutilayer • Track fit track parameters (a0, z0, f, cotq, 1./pt ) expressed at the first measured point

  6. MuonIdentification Method • MS track parameters are propagated to beam-axis • multiple scattering parameterised as scattering planes in calorimeters • energy loss from truth or from Calo Reconstruction or from parametrization as function of (h,p) • Refit • muon track parameters expressed at vertex • Muon/ID tracks matching with a c2 cut-off • c2 based on track covariance matrices and the difference in the track parameters • Combined track fit Muonspectrometer inner layer calorimeters Energy loss and multiple scattering Beam spot

  7. MooAlgs MooEvent Architecture (I) • C++ and OO technology • High modularisation and flexibility • Easier to develop alternative recostruction approaches • Successfully adapted for the test beam data reconstruction • Successfully integration in the HLT framework • Part of Moore will be used by Calib (MDT calibration) • Track class and Fitter class in common with the inner detector reconstruction (iPat) • Separation of the algorithmic classes • from data objects

  8. Architecture (II) Algorithms • Pattern recognition is divided in several steps. • Each step is driven by an Athena top-algorithm • Algorithms indepent, imply less dependencies, code more maintainable, modular, easier to develop new reconstruction approaches Moore Objects Separation of the algorithmic classes from data objects

  9. Parameterization of the Inert Material (I) March 2003 The treatment of the inert material until March • No services available yet in Athena for the description of the inert materials • The ATHENA geometry service allows to take into account in the fit multiple scattering and energy loss in the material of the chambers. • Work in progress (waiting for a Material Service from GeoModel): • Parameterization of the material effects: • Data driven approach: define a map of materials; tune materials with the pull distributions (available) • Geant4 based approach: describe all inert materials; propagate geantinos; define a fine map of materials in the Spectrometer (in progress) NO material in the fit 1/PT Pull Single m Pt=20 GeV Chamber Material in the fit

  10. 1.2 1.8 3.1 B C B 3.6 A 5.4 7.8 Parameterization of the Inert Material (II) • Main steps • Define a segmentation of the • Muon Spectrometer: • Binning in h/f/L • Estimate X0 and Energy Loss • in each h/f/L bin • Refit the track with 2 scattering • centers per station • Tune X0 and energy loss of the • “scatterers” against the pull • distributions (L = trajectory path length)

  11. Overall performances 1st iteration Pt = 6 GeV Pt = 20 GeV 1/pt pull distributions with Parameterization of Inert Materialand with Detector Material only |h| < 1 For fixed transverse momentum single muon samples |h| > 1

  12. Single m performances Efficiency vs pT • Single muons • DC1 data • Moore/MuonID performances shown here are obtained with • Release 6.0.3 • A private improved version of MuonIdentification • Tracking in the magnetic • fields, bug fixes • Moore with the full material description • MooAlgs-00-00-41 • MooEvent-00-00-42 PT (GeV) Rather good agreement with Physics TDR results

  13. Efficiency vs h Stepping in the fit procedure needs to be optimized in the region |h|>1 Uniform efficiency vs phi

  14. 1/Pt Resolution vs Pt Rather good agreement with Physics TDR results Pt (GeV)

  15. 1/Pt resolution vs h Critical reconstruction in the transition region at low momenta

  16. 1/Pt resolution vs f Worsening of resolution in the MuonSpectrometer in the feet region at low momenta

  17. s(1/pt pulls) vs h

  18. H4m (I)(Evelin Meoni) Reconstruction with Muon Spectrometer Standalone (Moore + MUID Standalone) • DC1 sample (prod. in July 2002): • H4m(with mH=130 GeV) • ~ 10 K evt. • ATHENA 6.0.3 (Moore-00-00-42) Without Z constraint With Z constraint  = (3.15±0.09) GeV  = (2.33±0.07) GeV Phy TDR  = 2.7 GeV Phy TDR  = 2.1 GeV

  19. H4m (II)(Evelin Meoni) Combined Reconstruction (Moore + MUID + iPat) Without Z constraint With Z constraint  = (1.49±0.05) GeV  = (1.85±0.06) GeV Phy TDR  = 1.6 GeV Phy TDR  = (1.42 ±0.06) GeV

  20. DC1 Data Moore/MUID in 6.0.2 Total Number of Events: ~10000 Z0→ Phy TDR: Standalone Muon Spectrometer : s = 3.0 GeV Combined MS + ID : s = 2.5 GeV

  21. Test Beam Reconstruction (I) • MuonTestBeam: ATHENA package designed to read and decode DAQ data from the Muon Test Beam in H8 and prepare the reconstruction with Moore algorithms • Status (in ATHENA release 6.2.0): • Converter to access to the H8-DAQ data in bytestream format  construct digits in the TDS using the new Muon Event Data Model • H8 detector description through the ATHENA MuonDetDescrMgr initialised from H8 Amdb • Segments and tracks reconstructed via Moore algorithms • Ntuples for the analysis • Access to digits from H8 Geant4 simulation also implemented • First tests performed on data collected during 2002 (only MDT) • Also RPC and TGC at this year’s test beam

  22. Test Beam Reconstruction (II) In the Athena framework DAQ file Test Beam Converter MuonDigitContainer Moore Algs. Ntuple Conditions DB service TDC counts Tracks angle First test on 2002 data • Straight line fit of track segments and full tracks implemented • Integration withMDT calibration, chambers alignment • The access to conditions DB through the Athena Interval Of Validity Service is under development

  23. Moore and MUID in HLT At the hearth of the philosopy of the Pesa design is the concept of seeding. Algorithms functioning in the context of the High Level Trigger do not operate in a general-purpose sense; rather they must validate or reject Trigger Element hypotheses formed at a previous stage in the triggering process. Reminder geometry

  24. Strategy for the Seeding RecoMuonRoI (h±Dh, f±Df) RegionSelector Hash offline IDs The HLT RegionSelector translates geometrical regions within the fiducial volume of the detector into a set of corresponding elements of appropriate granularity in each sub-detector The RecoMuonRoI gives eta & phi position of RoI. DigitsContainer seeded (RPCs and MDTs) TDS (ZEBRA) Using boths it is possible to build the seeded DigitsContainers RPCDigitContainer MDTDigitContainer PhiSegmentContainer RZSegmentContainer MooMakePhiSegmentSeeded MooMakeRZSegmentSeeded From the event store (ZEBRA) it is possible to access the full event and to build the RPC and MDT digitcontainers. And to follow the reconstruction chain MooMakeRoads MooMakeTracks MuId standalone Reconstructed Tracks

  25. Moore/MuId – First time-performance test PT (t -1) Moore • t-1 Average execution time per event calculated for the 500 events sample. • The 1st event has not been included in the calculation since in this event several services are initialized (magnetic field map,… ). MuId standalone

  26. Conclusions • A lot of improvements have been made to MOORE/MUID in the last two months: it can now be used for Physics Studies • The code has proved to be robust on high statistics DC1 samples (~106 events processed – No Crash) • A big (and successful!!) effort has been done for having MOORE/MUID as Event Filter in the HLT framework: results will appear in the HLT TDR • Alternative tracking methods to be inserted in MOORE (e.g. Kalman Filter) are under developments • We are aiming at keep going with the developments but always having a referenceversion to be used for Physics Studies

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