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

2. 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

3. 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

4. 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

5. 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

6. 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

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

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. 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

10. 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

11. 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

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

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

14. 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

15. 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 …