1. 1 OO Muon Reconstruction in ATLAS Michela Biglietti Univ. of Naples INFN/Naples Atlas offline software MuonSpectrometer reconstruction (Moore) Atlas combined reconstruction (MuonIdentification)

2. 2 Converter Algorithm Event Data Service Persistency Service Data Files Algorithm Transient Event Store Detec. Data Service Persistency Service Data Files Transient Detector Store Message Service JobOptions Service Particle Prop. Service Other Services Histogram Service Persistency Service Data Files Transient Histogram Store Application Manager Converter Necessity of a framework: a template application into which developers plug in their code, using mechanisms defined by the framework, collections of functionality, common vocabulary … Athena Offline software in Atlas

3. 3 Data flow Tracking Calorimetry Muon Tracks Em cluster Muon Calo Jets … … Combined Muon Analysis Raw digits Detector Descriptio n E/  identification Event, Identified particles MC & simulation Atlas Algorithm Event TDS Algorithm Alg2 Event Alg1 Alg3 Offline Reconstruction in Atlas

4. 4 Results : less dependencies, code is more maintainable, modular, easier to develop new reconstruction approaches Moore in Athena MooMakePhiSegments RPC/TGC digits PhiSegments MooMakeRZSegments MDT digits MooMakeRoads CrudeRZSegments MooMakeiPatTracks MooRoads MooiPatTracks MooMakeNtuples Ntuples MooAlgs MooStatistics Each step is driven by an Athena top- algorithm Transient objects are passed via TDS/StoreGate Independent algorithms, the only coupling is through the transient objects MooLVL2PhiSegmentMaker MooLVL2RZSegmentMaker Before: RPC/TGC/MDT digits Moore Ntuples Tracks Easier integration with other ATHENA packages to get services and for combined reconstruction, test-beam software, calibration, online/EF sw …

5. 5 MooAlgs MooAlgs_2 MooEvent MooAlgs_n MooCode Events for reconstruction Athena algorithms with different features/goals Shared code used by Athena Algos MooAlgsLVL2 MooStatistics Moore Packages

6. 6 Single muon studies A Muon track consists of hits from at least 2 stations and is successfully fitted.  (%) P T = 20 GeV P T = 100 GeV  = 3.3  = 3.4 Efficiency vs Pt Pt resolution Performance P T (GeV)

7. 7 MOORE: Pentium III 850 MHz - 256 Mbytes MUONBOX Pt(Gev)Time(ms) 2090 100300 570 10001500 Speed

8. 8 Combined Muon Reconstruction Improve muons identification efficiency – Discrimination of muons from  rays in the muon spectrometer – Reconstruction of low energy muons that do not reach the middle and outer stations of the muon spectrometer – Rejection of decay muons (from k and  ) by requiring tracks originate close the interaction point – Discrimination of muons in hadronic jets from hadrons. An efficient muon b-tagging requires a good muon identification for non isolated muons Improve track parameters – Achieve the best possible momentum resolution – Reduce tails in the momentm resolution of the muon spectrometer, resulted from fluctuation in energy loss in the calorimeter – Improve charge determination for high energy muons Understand the detector – Check the calibration of calorimeter. – Cross check the results from the inner detector and muon spectrometer (for muons with momenta from 20 GeV to 70 GeV)

9. 9 Muon Identification Pre-existing work: Muon Identification (MUID) package used for physic TDR  Atrecon implementation:  Input – results of ID, Calo and Muon reconstruction (Muonbox) (as C++ objects through interface packages)  Output – class structure => zebra banks => combined ntuple Purpose: associate tracks found in Muon Spectrometer with inner detector (ID) tracks and calorimeter information to identify muons at their production vertex with optimum parameter resolution 2 principle methods: 1.Stand-alone muons – Muon Spectrometer track and track-segment parameters propagated to beam-axis MS track and inner station segment parameters propagated to beam-axis Angle resolutions dominated by Coulomb scattering in calo Parametrise calorimeter effects – function of p and  (i.e. thickness) or measure energy loss from calibration of observed energy deposition MS track is express at vertex 2. Combined muons – match Muon Spectrometer to ID tracks and fit combined parameters  2 cut for matching of inner detector and muon spectrometer tracks parameters combined fit

10. 10 Muonidentification – Athena Implementation MuidStandAlone Moore Tracks CaloClusters MuidComb MuidTracks status standalone ID Tracks MuidTracks status combined MuidInit MuidTracks status muon TruthEvent Tracks MuidNtuple MuidIDNtuple MuidCombNtuple Ntuples

11. 11 Energy loss in the Calorimeters Total energy loss Tile Endcap hadronic LAr EM LAr reconstructed (GeV) from MC-Truth (GeV) Pt = 100 GeV Pt = 20 GeV Pt = 300 GeV

12. 12 cot  pulls Single  Pt = 20 GeV Single  Pt = 20 GeV StandAlone Tracks : pulls @vertex  pulls

13. 13 Pt @MS entrance (Moore) Pt @vertexPt @MS entrance (Moore) Pt @vertex Pt = 20 GeV Pt = 100 GeV Pt corrections @vertex

14. 14 Muon Track (Moore + Calo + Muid) InDet (iPatRec) Combined (Muid)  = 2.9  = 5.2  = 2.6 Pt = 100 GeV  = 3.6  = 2.1  = 2.0 Pt = 20 GeV Muon Track (Moore + Calo + Muid) InDet (iPatRec) Combined (Muid) Pt = 300 GeV  = 3.9  = 12.5  = 3.8 Pt Resolutions & Combination

15. 15Conclusions Moore – What is needed Description of inert material EDM implemantation Layout P – DC1 data reconstruction – Items Material, EDM, testbeam version, geometry/event description, repackaging/intergration, LVL2 … MuonIdentification – to do Energy loss parametrisation Fit-tracking optimization Calorimeter multiple scattering tuning Integration with the new version of Moore (material description and EDM) Better design, full debug …