Transcript Document 7402222

ATLAS muon system performance
ATLAS Trigger and Physics Workshop
Muon Trigger and Combined Performance Session
David Adams
Brookhaven National Laboratory
May 29, 2006
ATLAS
Updated 19:00 May 29, 2006
Contents
Goals
AOD muons
Selecting muons
Performance evaluation
• Monte Carlo muons
• Matching
• Performance parameters
Example results
Summary results
• Type contributions to
efficiency and fakes
• Misreconstructed fraction
• Type contributions to good
efficiency and misrecos
• Resolutions
• PTsum tail
Summary comments
Issues
Conclusions
• Efficiency
• Fakes/event
• Fakes/reals
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Goals
Assess the performance of the muon reconstruction software
• Release 11.0.X
• CSC production samples
Examine the view presented for physics analysis
• Two AOD collections
– MuidMuonCollection
– StacoMuonCollection
• Learn how to select a sensible set of muons from these collections
– Kinematic and quality cuts
Establish metrics for evaluating performance
• Efficiency, fake rate, resolutions, …
• For different selections
• As functions of kinematic variables
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AOD muons
AOD has two muon containers
• StacoMuonCollection
• MuidMuonCollection
Each muon has one or more of four track particles
• inDetTrackParticle
– Inner detector track, e.g. from low-pT algorithm
• muonSpectrometerTrackParticle
– From standalone muon finding
– Parameters at entrance to the muon system
• muonExtrapolatedTrackParticle
– Previous extrapolated to the beamline DCA
• combinedMuonTrackParticle
– Combination of inner and standalone muon tracks
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AOD muons
I divide these into four categories
• Combined (0100)
– Have a combined inner detector and muon spectrometer track,
– are “best match” and
– have good fit and match chi-squares
• Split (1000)
– Combined with bad fit or match chi-square
– Inner detector track is used
• Standalone (0010)
– Tracks which have an muon spectrometer track propagated to the
beam line
– Excluding combined or split if enabled
• Inner (0001)
– Muons which have an inner detector track but do not have a muon
spectrometer track
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AOD muons (cont )
Quality parameters
• Needed to distinguish real from fake muons
• Fit and match chi-squares are present but don’t provide much
discrimination
• Isolation helps but we don’t want to lose all non-isolated muons
• There are methods to return the number of hits for each detector
type but they return incorrect values
• Associated digit counts are available only for Muid low-pT
– I use a crude cut to reduce the number of Muid low-pT fakes
• It would be very helpful to have information about misses, i.e.
active detectors that were crossed with no hit or segment found
– Inner detector calls these “holes” and, for selected subdetectors,
records the number of such layers
– Complicated muon geometry would probably be better handled with
some sort of likelihood estimate for each miss and/or the whole track
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Selecting muons
I use the following job options to select muons:
• Name of the muon container (Staco… or Muid…)
• Flags indicating which categories of muons to accept
– I consider three cases for each of the two containers:
> 0010 = standalone only
> 0100 = combined only
> 1111 = all four (combined, split, standalone and inner)
• Kinematic cuts
– pT above some threshold (3 GeV/c in the following)
– abs(z0) (z at the DCA) below some value (not used)
• Fit chi-square limit for all track types
– Fit chi-square/DOF (below 100.0)
– Loose to allow for kinks (brem); tighter cuts follow
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Selecting muons
• Chi-square limits to define good combined muons
– Fit chi-square/DOF below 5.0
– Match chi-square below 20.0
– Those above these values are “split”
• Isolation
– Muon is said to be isolated if it has an isolation energy less than 10
GeV (0.45. cone)
– Split, standalone and inner muons are required to be isolated
– Non-isolated, combined muons are subject to tight chi-square cuts
> Fit chi-square below 2.5
> Match chi-square below 10.0
• Associated digit counts
– Muid low-pT (inner) muons are required to have more than 10 digits
(precision or trigger)
– To reduce the large number of fakes
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Selecting muons
Overlaps
• Muons are said to have spatial overlap if they are within dR of 0.50
• The following are removed:
– Split, standalone and inner muons overlapping a combined muon
– Split and standalone muons overlapping an inner muon
– Inner muons overlapping an inner muon with smaller fit chi-square
Shared tracks
• Muons may removed if they share tracks with another muon
• The following would be removed
– Combined muons sharing any track with a combined muon with larger match
chi-square
– Split, standalone and inner sharing any track with a combined muon
– Split muons sharing an inner track with an inner muon.
• However, shared track cuts were not applied because some muons in the
Muid container have invalid track assignments
– Expect that shared track filter is redundant with overlap filter
– Future studies will likely add shared filter and reduce the filtering in the
overlap filter
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Selecting muons
Cut summary
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Container: Muid or Staco
Categories: standalone (0100), combined (0010) or all (1111)
Kinematics: pT > 3 GeV/c, no cut on z0
Chi-square/DOF (all muons): below 100.0
Combined/split discrimination (split if either fails)
– Fit chi-square/DOF below 5.0
– Match chi-square below 20.0
• Isolation: 10 GeV/c in 0.45 cone
– Split, standalone and inner muons required to be isolated
• Chi-squares for non isolated combined muons:
– Fit chi-square/DOF below 2.5
– Match chi-square below 10.0
• Muid inner muons required to have more than 10 digits
• Overlaps removed if dR below 0.5
• Shared track filter disabled
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Performance evaluation
For Monte Carlo samples, we can evaluate the performance
of the muon reconstruction using truth information
• Select a collection of reconstructed muons (evaluation sample)
• Select a collection of Monte Carlo muons (reference sample)
• Match the two (one reco to one MC)
– Efficiency is (# matched MC)/(# MC)
– Fake rate is the (# unmatched reco)/event
– Compare matched reco and MC parameters to calculate resolutions
and fraction of misreconstructed tracks
Following sections describe
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Selection of MC muons
Matching algorithm
Precise definitions of efficiency, fake rate, …
Results for selected CSC samples
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Monte Carlo muons
Monte Carlo muons are taken from the truth particle container
• Includes prompt muons and those produced in the tracking volume
• Assume all such muons (above our pT threshold) are recorded
Monte Carlo selections:
• Loose selection to construct the reference sample for matching
– Use final state particles (ATLAS algorithm)
– pT above job option threshold (2 GeV/c in the following)
– PDG ID must correspond to muon (+/-13)
• Tight selection to evaluate efficiency (after matching)
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Increase pT threshold (4 Gev/c)
Maximum value for eta (2.7)
Maximum value for muon production radius (2 cm)
All can be set with job options
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Matching
Each reconstructed track is matched to the nearest MC track
• Using reco-to-MC distance DR = sqrt[(TR – TMC) WR (TR – TMC)]
– TR = reconstructed track vector
– WR = inverse of error matrix for track vector
– TMC = Monte Carlo value for the track vector
• If no MC track is found within some maximum distance DRmax,
then the reconstructed track is left unmatched
– In the following, DRmax is set to 1000
A MC-to-reco distance is calculated for each match: DMC =
sqrt[((jR-jMC)/Dj)2 + ((hR-hMC)/Dh)2 + ((pTR-pTMC)/pTMC/DpTrat)2 ]
• Matches with DMC > DMCmax are dropped
• DMCmax, Dj , Dh and DpTrat are all job options
– For this study: 100, 0.005, 0.005 and 0.03 were used
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Matching (cont)
Only one reco track is allowed to match each MC track
• Matches with larger DMC are dropped
• Not DR because it favors tracks with large errors
Tracks classified according to their match status
• Matched tracks are found
• Unmatched MC tracks are lost
• Unmatched reco tracks are fake
Found tracks are well-reconstructed (good) if
• DR < DRgood and DMC < DMCgood
– Limits are job options
– Both set to 10 for this study
• Good tracks are used to evaluate resolutions
• Tracks found but not good are called bad or misreconstructed
Efficiency and resolutions are calculated using tight MC cuts
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Matching (cont)
Reco
MC
Found
Fake
Lost
Matching example
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Matching (cont)
Reco-to-MC distance
whbb, Muid combined
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Matching (cont)
MC-to-reco distance
whbb, Muid combined
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Matching (cont)
MC parameter summary
• Efficiency is presented for all MC muons with
– pT > 4 GeV/c
– eta < 2.7
– r0 < 2.0 cm
• Found muons are labeled as “good” if
– DMC < 10.0
– DR < 10.0
 Dj = Dh = 0.005
> DpTrat = 0.03
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Performance parameters
The following are used to evaluate the performance
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Efficiency = (# found) / (# MC)
Good fraction = (# good) / (# found)
Misreco fraction = 1 – (good fraction)
Good efficiency = (# good) / (# MC)
Fakes/event = (# fake) / (# events)
Fakes/reals = (# fake) / (# found)
Resolution in X = RMS of (XR – XMC)
– X may be h, j, pT or any of the track parameters
– Evaluated for good (well-reconstructed) tracks
In addition, pTsum tails are calculated
• Each event, vector sum over pT is calculated for both MC and reco muons
• The magnitude of the vector difference between these sums is calculated
• Fraction of events with this difference exceeding some threshold is the
pTsum tail
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Example results
Following slides show some example plots
• Efficiency
• Fakes
• pT sum
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Found
Lost
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Fakes
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MC
Found
Residual
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Summary results
Summary results are presented on the following pages
• Six sets of muons:
– Staco and Muid for three cases
> Standalone
> Combined
> All (combined, split, standalone and inner)
• Eleven datasets
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s050 - csc11.007231.singlepart_mu50.recon.AOD.v11004103
s100 - csc11.007233.singlepart_mu100.recon.AOD.v11004103
zmm - csc11.005151.McAtNloZmumu.recon.AOD.v11004103
wmu - csc11.005101.JimmyWmunu.recon.AOD.v11004103
whbb - csc11.005850.WH120bb_pythia.recon.AOD.v1100410
h1304l - csc11.005300.PythiaH130zz4l.recon.AOD.v11004103_bnl
h170wwll - csc11.005320.PythiaH170wwll.recon.AOD.v11004107
G500mm - csc11.005621.Gmm_500_pythia_photos.recon.AOD.v11004103
j1 - csc11.005010.J1_pythia_jetjet.recon.AOD.v11004103
j3 - csc11.005012.J3_pythia_jetjet.recon.AOD.v11004103
j6 - csc11.005015.J6_pythia_jetjet.recon.AOD.v11004103
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Efficiency
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Fakes/event
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Fakes/reals
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Type contributions to efficiency and fakes
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Misreconstructed fraction
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Type contributions to good efficiency and misrecos
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Resolutions
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Resolutions (cont)
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Resolutions (cont)
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Resolutions (cont)
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Resolutions (cont)
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Resolutions (cont)
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Resolutions (cont)
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Resolutions (cont)
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pT sum tail
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pT sum tail (cont)
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pT sum tail (cont)
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Summary comments
Standalone
• Both Muid and Staco do a good job of finding isolated muons
– Efficiency: 95% in single muon, Zmm and Wm samples
– 80% in busier Higgs events
– Falls dramatically in high-pT jets
> Due to isolation cut required to reduce fakes
> Need some work here to improve crude isolation cut
 Probably best to calculate a likelihood during reconstruction
that can be used to discriminate between reals and fakes
• Fakes are 2-5X worse in Muid
– In j6, Muid is 0.02/event, Staco is 0.007/event
• Resolutions and pT sum residuals are similar
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Summary comments (cont)
Combined
• Staco efficiency is better
– 3-5% in single muons and high-pT samples
– 10-15% in jets
• Efficiencies flat around 88% for Staco
– Much of the decrease from standalone is due to the eta coverage of
inner tracker
• Fakes, resolutions and pt sum residuals are similar for Staco and
Muid
– Fakes up to 0.05/event in j6
• Muid has 3-8X fewer misreconstructed muons in high-pT samples
– But Staco and Muid are the same in the dijet events
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Summary comments (cont)
All (combined, split, standalone and inner)
• Staco and Muid efficiencies similar in high pT events: 95%
– In jets, Staco performs better
> For j6, Staco is 86%, Muid is 73%
• Staco fakes range from comparable to 2X less than Muid
– Muid getting efficiency boost and fakes from inner muons
• Misreconstructed fractions and resolutions are similar
– Staco has slightly better pT resolution
– Muid is slightly better with eta and phi resolutions
• pT sum residual
– In both cases, combined does better than all
> Less fakes and misrecos more important than extra efficiency
(mostly at high eta)
– Staco tails are generally 2-3X lower than those for Muid
> But Staco and Mui jet results for combined muons are very
similar
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Issues
Hit counts are missing or wrong in the Muid AOD
• Could use this as a measure of track quality
There are incorrect track assignments in the Muid AOD
• Cannot filter muons with shared tracks
Poor fit quality at high eta
• Presumably due to CSC problems
Need better AOD handles to discriminate against fakes for
inner (low-pT) tracks
• E.g. likelihood that track would produce the observed signals (and
absence of signals) in the muon detectors
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Issues (cont)
Like to have track positions at muon spectrometer “entrance”
• To filter standalone overlaps
• Some common surface (cylinder for central, z-plane for endcaps)
• Or can (and should) we use the muon spectrometer track particle?
Like to have truth muons produced outside tracking volume
• To identify “fakes” due to pi/K decays in calorimeter
Like to make use of calorimeter information in finding
• Find muons where muon spectrometer is blind
• E.g. Wisconsin topo cluster method
• YATA (Yet Another Tracking Algorithm)
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Conclusions
It is now (release 11.0.X) possible to analyze AOD muons from both
Staco and Muid
• Very similar analysis algorithms
• Allows direct comparison
Good performance for combined muons for either collection
• Efficiency 80-90%
• Fakes around 0.01/event approaching 0.1/event in high-pT jets
Addition of split, standalone and inner muons improve performance
• Efficiency increases to 95% for high-pT muon
– Biggest gain at high eta
• Fakes kept at similar levels with isolation cuts
– Require all split, standalone and inner muons to be isolated
– Apply tighter cuts for non-isolated combined muons
Need better discriminants (and more work) to simultaneously improve
efficiency and fake rejection
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