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

Offline software in Atlas 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 Converter Algorithm Event Data Service Persistency Data Files Transient Event Store Detec. Data Transient Detector Store Message JobOptions Particle Prop. Other Services Histogram Transient Histogram Store Application Manager

Offline Reconstruction in Atlas Algorithm Event Alg1 Event T D S Algorithm Event Event Alg2 Event Algorithm Event Alg3 Event Raw digits Atlas Data flow MC & simulation … … Tracking Tracks Detector Description E/g identification Event, Identified particles Em cluster Calorimetry Calo Jets Combined Muon Muon Muon Analysis

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

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

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

Speed MOORE: MUONBOX Pt(Gev) Time(ms) 20 90 100 300 570 1000 1500 Pentium III 850 MHz - 256 Mbytes MUONBOX Pt(Gev) Time(ms) 20 90 100 300 570 1000 1500

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)

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: 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 h (i.e. thickness) or measure energy loss from calibration of observed energy deposition MS track is express at vertex 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

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

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

StandAlone Tracks : pulls @vertex Single  Pt = 20 GeV cotq pulls F pulls

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

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

Conclusions Moore MuonIdentification What is needed Items to do 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 …