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  performances H→4  Z→2  Test beam data reconstruction Moore and Muid as Event Filter algorithms Conclusions

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

MOORE Reconstruction Strategy (I) rpc MDT barrel  projection barrel RZ projection Search for region of activity in the  projection and RZ projection rpc rpc rpc

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.  Track fit track parameters (a0, z0, , cot , 1./pt ) expressed at the first measured point MDT mutilayer MDT pattern recognition

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 ( ,p) Refit muon track parameters expressed at vertex Muon/ID tracks matching with a  2 cut-off  2 based on track covariance matrices and the difference in the track parameters Combined track fit calorimeters Muonspectrometer inner layer Beam spot Energy loss and multiple scattering

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) MooAlgs MooEvent Separation of the algorithmic classes Separation of the algorithmic classes from data objects

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

Parameterization of the Inert Material (I) NO material in the fit 1/P T Pull Single  Pt=20 GeV Chamber Material in the fit 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 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) March 2003

A BBC Parameterization of the Inert Material (II) Main steps Define a segmentation of the Muon Spectrometer: Binning in  /  /L Estimate X0 and Energy Loss in each  /  /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)

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

Single muons DC1 data P T (GeV) Efficiency vs p T Rather good agreement with Physics TDR results Moore/MuonID performances shown here are obtained with Release A private improved version of MuonIdentification Tracking in the magnetic fields, bug fixes Moore with the full material description MooAlgs MooEvent Single  performances

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

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

1/Pt resolution vs  Critical reconstruction in the transition region at low momenta

1/Pt resolution vs  Worsening of resolution in the MuonSpectrometer in the feet region at low momenta

 (1/pt pulls) vs 

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

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

Z 0 →  DC1 Data DC1 Data Moore/MUID in Moore/MUID in Total Number of Events: ~10000 Total Number of Events: ~10000 Phy TDR: Standalone Muon Spectrometer :  = 3.0 GeV Combined MS + ID :  = 2.5 GeV

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

Test Beam Reconstruction (II) 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 Straight line fit of track segments and full tracks implemented Integration with MDT calibration, chambers alignment Integration with MDT calibration, chambers alignment The access to conditions DB through the Athena Interval Of Validity Service is under development The access to conditions DB through the Athena Interval Of Validity Service is under development In the Athena framework

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

Strategy for the Seeding RecoMuonRoI (  ±     ±   ) RegionSelector Hash offline IDs DigitsContainer seeded (RPCs and MDTs) RPCDigitContainer MDTDigitContainer TDS (ZEBRA) MooMakePhiSegmentSeeded MooMakeRZSegmentSeeded PhiSegmentContainer RZSegmentContainer MooMakeRoads MooMakeTracks MuId standalone Reconstructed Tracks 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. From the event store (ZEBRA) it is possible to access the full event and to build the RPC and MDT digitcontainers. Using boths it is possible to build the seeded DigitsContainers And to follow the reconstruction chain

Moore/MuId – First time-performance test 20GeV TDR20GeV DC1300GeV TDR 200GeV DC1 H 4  DC1 142 msec155 msec368 msec279 msec572 msec (t -1) t-1 Average execution time per event calculated for the 500 events sample. t-1 Average execution time per event calculated for the 500 events sample. The 1 st event has not been included in the calculation since in this event several services are initialized (magnetic field map,… ). PT Pt (GeV)Time (ms) H->4mu m H = 130 GeV 25.2

ConclusionsConclusions  A lot of improvements have been made to MOORE/MUID in the last two months: 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 (~10 6 events processed – No Crash) (~10 6 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 MOORE/MUID as Event Filter in the HLT framework: results will appear in the HLT TDR appear in the HLT TDR  Alternative tracking methods to be inserted in MOORE (e.g. Kalman Filter) are under developments Filter) are under developments  We are aiming at keep going with the developments but always having a reference version to be used for Physics Studies having a reference version to be used for Physics Studies