Muon Reconstruction with Moore and MuonIdentification
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Transcript of Muon Reconstruction with Moore and MuonIdentification
Muon Reconstruction Muon Reconstruction with Moore and with Moore and
MuonIdentificationMuonIdentification
Muon Reconstruction Muon Reconstruction with Moore and with Moore and
MuonIdentificationMuonIdentification
The Moore/MUID group
Atlas Physics Workshop
Athens, May 2003
OutlineOutline 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
TasksTasks Moore Moore (Muon Object Oriented REconstruction)
reconstruction in the MuonSpectrometer MuonIdentificationMuonIdentification
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 MOORE Reconstruction Reconstruction Strategy (I)Strategy (I)
MOORE MOORE Reconstruction Reconstruction Strategy (I)Strategy (I)
rpc
rpc
rpc
MDT
barrelbarrel projectionprojection
barrelbarrelRZ projectionRZ projection
Search for region Search for region of activity in the of activity in the
projectionprojectionand and RZ projection RZ projection
rpcrpc
rpcrpc
rpcrpc
MOORE Reconstruction Strategy(II)MOORE Reconstruction Strategy(II)
Pattern recognition in the Pattern recognition in the MDTsMDTs the drift distance is calculated the drift distance is calculated
from the drift time, by applying from the drift time, by applying various corrections on it (TOF, various corrections on it (TOF, second coordinate, propagation second coordinate, propagation along the wire, Lorenz effect). along the wire, Lorenz effect). Among the 4 tangential lines the Among the 4 tangential lines the best one is found.best one is found.
Track segment combinationTrack segment combination. .
Track fitTrack fittrack parameters (a0, z0, track parameters (a0, z0, , cot, cot, 1./pt ) expressed , 1./pt ) expressed at the first measured pointat the first measured point
MDT mutilayer
MDT patternMDT patternrecognitionrecognition
MuonIdentification Method 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
calorimeterscalorimeters
Muonspectrometer inner layer
Beam spot
Energy loss and multiple scattering
Architecture (I)Architecture (I) C++ and OO technology High modularisation and flexibility
Easier to develop alternative recostruction approaches Successfully adapted for the test beam data reconstructionSuccessfully integration in the HLT frameworkPart 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 objectsfrom data objects
Architecture (II)Architecture (II)Pattern recognition is divided Pattern recognition is divided
in several steps.in several steps.
Each step is driven by an Each step is driven by an Athena top-algorithmAthena top-algorithm
Algorithms indepent, imply Algorithms indepent, imply less dependencies, code more less dependencies, code more maintainable, modular, easier maintainable, modular, easier to develop new reconstruction to develop new reconstruction approaches approaches
Separation of the algorithmic classes from data objects
Parameterization of the Inert Material (I)Parameterization of the Inert Material (I)
NO material
in the fit
1/PT Pull
Single Pt=20 GeV
Chamber
Material in the fit
The treatment of the inert material The treatment of the inert material
until Marchuntil March
No services available yet in Athena for the description of the inert materials
The ATHENA geometry service allowsThe 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
BB C
Parameterization of the Inert Material (II)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)
1.2
1.8
3.13.6
5.47.8
Overall performances 1st
iteration
Overall performances 1st
iteration1/pt pull distributions with Parameterization of Inert Material and with Detector Material only
For fixed transverse momentum single muon
samples
Pt = 6 GeVPt = 6 GeV Pt = 20 GeVPt = 20 GeV
||| < 1| < 1
||| > 1| > 1
Single muons DC1 data
PT (GeV)
Efficiency vs pT Efficiency vs pT
Rather good agreement with
Physics TDR results
Moore/MuonID performances shown here are obtained with
• Release 6.0.3• A private improved version of MuonIdentification
•Tracking in the magnetic fields, bug fixes
• Moore with the full material description
MooAlgs-00-00-41
MooEvent-00-00-42
Single performancesSingle performances
Efficiency vs Efficiency vs
Stepping in the fit procedure needs to be optimized in the region |Stepping in the fit procedure needs to be optimized in the region ||>1|>1
Uniform efficiency vs phi
1/Pt Resolution vs Pt1/Pt Resolution vs Pt
Rather good agreement
with Physics TDR results
Pt (GeV)
1/Pt resolution vs 1/Pt resolution vs
Critical reconstruction in the transition region at low momenta
1/Pt resolution vs 1/Pt resolution vs
Worsening of resolution in the MuonSpectrometer in the feet region at low momenta
(1/pt pulls) vs (1/pt pulls) vs
Without Z constraintWithout Z constraintWithout Z constraintWithout Z constraint
Reconstruction with Muon Spectrometer Standalone (Moore + MUID Standalone)
Reconstruction with Muon Spectrometer Standalone (Moore + MUID Standalone)
With Z constraintWith Z constraintWith Z constraintWith Z constraint
HH44(I)(I)Evelin MeoniEvelin Meoni•DC1 sample (prod. in July 2002):DC1 sample (prod. in July 2002): HH44 (with mH=130 GeV) (with mH=130 GeV) ~ 10 K evt.~ 10 K evt.• ATHENA 6.0.3 (Moore-00-00-42)ATHENA 6.0.3 (Moore-00-00-42)
•DC1 sample (prod. in July 2002):DC1 sample (prod. in July 2002): HH44 (with mH=130 GeV) (with mH=130 GeV) ~ 10 K evt.~ 10 K evt.• ATHENA 6.0.3 (Moore-00-00-42)ATHENA 6.0.3 (Moore-00-00-42)
= (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 constraintWith Z constraintWith Z constraintWith Z constraint
= (1.85±0.06) GeV
Without Z constraintWithout Z constraintWithout Z constraintWithout Z constraint
Phy TDR = 1.6 GeV Phy TDR = (1.42 ±0.06) GeV
Combined Reconstruction (Moore + MUID + iPat)Combined Reconstruction (Moore + MUID + iPat)
HH44(II)(II)Evelin MeoniEvelin Meoni
Z0→ Z0→ DC1 Data DC1 Data Moore/MUID in 6.0.2Moore/MUID in 6.0.2 Total Number of Events: Total Number of Events:
~10000~10000
Phy TDR:Standalone Muon Spectrometer : = 3.0 GeVCombined MS + ID : = 2.5 GeV
Test Beam Reconstruction (I)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)Test Beam Reconstruction (II)DAQ DAQ filefile Test Beam ConverterTest Beam Converter
MuonDigitContainerMuonDigitContainer Moore Algs.Moore Algs. NtupleNtuple
Conditions DB Conditions DB serviceserviceTDC TDC
countscountsTracks Tracks angleangle
First test on First test on 2002 data 2002 data
• Straight line fit of track segments and full tracks implemented Straight line fit of track segments and full tracks implemented • Integration withIntegration with MDT calibration, chambers alignmentMDT calibration, chambers alignment
• The access to conditions DB through the Athena Interval Of Validity The access to conditions DB through the Athena Interval Of Validity Service is under developmentService is under development
In the Athena In the Athena frameworkframework
Moore and MUID in HLTMoore and MUID in HLTAt 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
Strategy for the Seeding
RecoMuonRoI
( ±±)RegionSelector Hash offline IDs
DigitsContainerseeded
(RPCs and MDTs)
RPCDigitContainerMDTDigitContainer
TDS(ZEBRA)
MooMakePhiSegmentSeededMooMakeRZSegmentSeeded
PhiSegmentContainerRZSegmentContainer
MooMakeRoadsMooMakeTracksMuId 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 testMoore/MuId – First time-performance test
20GeV TDR 20GeV DC1 300GeV TDR
200GeV DC1
H 4 DC1
142 msec 155 msec 368 msec 279 msec 572 msec(t -1)(t -1)
• t-1 t-1 Average execution time per event calculated for the 500 events sample. Average execution time per event calculated for the 500 events sample.The 1The 1stst event has not been included in the calculation since in this event several event has not been included in the calculation since in this event several services are initialized (magnetic field map,… ). services are initialized (magnetic field map,… ).
PTPT
Pt (GeV) Time (ms)
20 5.1
100 6.3
300 4.9
H->4mu
mH= 130 GeV
25.2
ConclusionsConclusionsConclusionsConclusions
A lot of improvements have been made to MOORE/MUIDA 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 samplesThe code has proved to be robust on high statistics DC1 samples (~10(~1066 events processed – No Crash) events processed – No Crash)
A big (and successful!!) effort has been done for having 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 TDRappear in the HLT TDR
Alternative tracking methods to be inserted in MOORE (e.g. KalmanAlternative tracking methods to be inserted in MOORE (e.g. Kalman Filter) are under developmentsFilter) are under developments We are aiming at keep going with the developments but alwaysWe are aiming at keep going with the developments but always
having a having a referencereference versionversion to be used for Physics Studies to be used for Physics Studies