Transcript Moore Organization

OO Muon
Reconstruction in
ATLAS
• Atlas offline software
• MuonSpectrometer reconstruction (Moore)
• Atlas combined reconstruction (MuonIdentification)
Michela Biglietti
Univ. of Naples INFN/Naples
1
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 …
Converter
Converter
Converter
Application
Manager
Message
Service
JobOptions
Service
Particle Prop.
Service
Other
Services
Athena
Event Data
Service
Persistency
Service
Data
Files
Transient
Event Store
Algorithm
Algorithm
Algorithm
Detec. Data
Service
Transient
Detector
Store
Persistency
Service
Data
Files
Histogram
Service
Transient
Histogram
Store
Persistency
Service
Data
Files
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Offline Reconstruction in Atlas
Algorithm
Alg1
Event
Algorithm
Alg2
Algorithm
Alg3
Raw
digits
Atlas
MC & simulation
Event
Event
Event
Em
cluster
Calorimetry
Calo Jets
Muon
Muon
S
Event
Event
Data flow
Tracking
Tracks
D
Event
…
Detector
Descriptio
n
T
E/g
identification
…
Event,
Identified
particles
Combined
Muon
3
Analysis
Moore in Athena
Before:
Moore
RPC/TGC/MDT digits
MooAlgs
Tracks
RPC/TGC digits
MooMakePhiSegments
MooLVL2PhiSegmentMaker

PhiSegments
MooMakeRZSegments
MooLVL2RZSegmentMaker

MDT digits
MooMakeRoads
CrudeRZSegments
MooMakeiPatTracks
MooStatistics
Ntuples
MooRoads

Each step is driven by
an Athena topalgorithm
Transient objects are
passed via
TDS/StoreGate
Independent
algorithms, the only
coupling is through
the transient objects
MooiPatTracks
MooMakeNtuples
Ntuples
Easier integration
with other code
ATHENA
Results
: less dependencies,
is more
packages to get
services
and for
maintainable,
modular,
easier
to develop
combined
reconstruction,
test-beam4
new
reconstruction
approaches
software, calibration, online/EF sw …
Athena algorithms with
different features/goals
Moore Packages
MooAlgs_2
MooAlgsLVL2
MooAlgs
MooStatistics
MooAlgs_n
MooCode
Shared code used by
Athena Algos
MooEvent
Events for reconstruction
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Performance
 (%)
Single muon
studies
Efficiency vs Pt
A Muon track consists of
hits from at least 2 stations
and is successfully fitted.
PT = 20 GeV
PT (GeV)
PT = 100 GeV
 = 3.4
 = 3.3
Pt resolution
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Speed
MOORE:
•Pentium III 850 MHz - 256 Mbytes
Pt(Gev)
Time(ms)
20
90
100
300
300
570
1000
1500
MUONBOX
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Combined Muon Reconstruction

Improve muons identification efficiency
– Discrimination of muons from  rays in the muon
–
–
–

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
–
–
–

spectrometer
Reconstruction of low energy muons that do not
reach the middle and outer stations of the muon
spectrometer
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)
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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

2 principle methods:
detector (ID) tracks and calorimeter information to identify muons at
their production vertex with optimum parameter resolution
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 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.


2
cut for matching of inner detector and muon spectrometer tracks parameters
combined fit
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Muonidentification – Athena
Implementation
MuidInit
Moore Tracks
TruthEvent Tracks
MuidStandAlone
MuidTracks status muon
CaloClusters
MuidComb
MuidTracks status standalone
MuidNtuple
ID Tracks
MuidIDNtuple
MuidCombNtuple
MuidTracks status combined
Ntuples
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Energy loss in the
Calorimeters reconstructed (GeV)
Pt = 20 GeV
Pt = 100 GeV
Pt = 300 GeV
Total
energy loss
Tile
Endcap
hadronic LAr
EM LAr
from MC-Truth (GeV)
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StandAlone Tracks :
pulls @vertex
cotq pulls
Single 
Pt = 20 GeV
F pulls
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Pt corrections @vertex
Pt = 20 GeV
Pt @MS
entrance
(Moore)
Pt @vertex
Pt = 100 GeV
Pt @MS
entrance
(Moore)
Pt @vertex
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Muon Track
(Moore +
Calo + Muid)
Pt Resolutions
& Combination
InDet
(iPatRec)
Combined
(Muid)
Muon Track
(Moore +
Calo + Muid)
InDet
(iPatRec)
Combined
(Muid)
Pt = 100 GeV
Pt = 20 GeV
 = 3.6
 = 2.1
 = 2.0
Pt = 300 GeV
 = 2.9
 = 3.9
 = 5.2
 = 12.5
 = 2.6
 = 3.8
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Conclusions

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 …
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