Transcript PartridgeSLUOJuly09

Upgrade Tracker
Simulation Studies
Richard Partridge – SLAC
SLUO LHC Workshop
Simulation Can Help…
Optimize Tracker Geometry
 Identify number of layers needed for robust tracking
 Locate transitions between pixels, short and long strips
 Evaluate options for placement of tracking layers
Optimize Stave / Module Design
 Compare performance of design alternatives
Optimize Placement of Services
 Determine performance impact of dead material
 Investigate alternatives for routing of services
Quantify Detector Performance
 Measure tracker and physics performance benchmarks
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Simulation Challenges
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Challenging environment for track finding
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Detailed / realistic simulations essential
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Expect >106 hits in tracker just from pileup interactions – need
sufficient layers to beat down combinatoric fake track rate
Non-negligible dead material – multiple scattering and secondary
interactions are important factors
Cost and material dictate small number of layers – cannot afford $ or
material to grossly over-design
Pattern recognition is the key issue – tracker must be capable of
efficiently finding real tracks with a low rate for fake tracks
Need to have confidence that simulations are accurately measuring
tracker performance, not limits of simulation software
Flexibility to make comparative studies
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Optimizing the tracker design requires the ability to compare design
alternatives without extensive code changes
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Simulation Tools
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Athena / Geant 4
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ATLSIM / Geant 3
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Used for early ATLAS simulations
Very detailed description of current detector
Some geometry changes easy, some are hard
LCSim / Geant 4
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Adapt existing ATLAS detector simulation to upgrade geometry
New layers overflow 32 bit identifier scheme  64 bit identifiers
Need to accommodate new endcap geometry
Apply Linear Collider simulation tools to ATLAS upgrade
Compact geometry description  geometry changes are easily made
Tools designed specifically for realistic detector optimization
Fatras (modest SLAC contribution)
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Fast hit simulation from MC generated particles
Material description extracted from Athena / Geant 4
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Why Have Multiple Tools?
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Cross check / verification of results
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Different tools have different strengths
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Want to make sure we are measuring intrinsic detector performance,
not the features / limitations of our tools
If we get consistent results from independent set of tools, we are
probably measuring detector performance
Where results are different, we may also learn something useful about
simulation assumptions and/or algorithm behavior
Athena/G4 builds on existing ATLAS tools
Many early simulation results produced with ATLSIM
LCSim has flexible and easily modified geometry description
Fatras provides fast simulations with standard ATLAS track finding
Some “friendly competition” is usually a good thing
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Spurs innovation, challenges assumptions / prejudices
Some VERY preliminary results follow
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Pileup Interactions
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Low pT pileup interactions dominate tracker occupancy
For L = 1035, ~400 interactions/xing at 50 ns spacing
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25 ns beam spacing and/or luminosity leveling would lower #int/xing
Higher luminosity, new contributions to the inelastic cross section
would increase #int/xing
Number of charged particles
per interaction (dN/dh) also
has some uncertainty
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Moraes et al, EPJ C 50, 435 (2007)
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Charged Particle Multiplicity at L = 1035
ATLAS Tune: dN/dh = 6.8 / Int
Tevatron Tune: dN/dh = 5.6 / Int
(5.3 if you remove diffractives)
Diffractives: dN/dh = 0.3 / Int.
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No pT Cut
Tracker Occupancy
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Even with silicon pixel and strip sensors, hit occupancy
is not small
Use short (2.5 cm long) strips at intermediate radius to
reduce occupancy
Short
Strips
Long
Strips
Pixels
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Secondary Interactions
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Each tracker layer contributes 2-3% X0 of material
Origin of non-prompt charged particles shows
substantial contribution from secondary interactions
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Tracking Efficiency
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Tracking efficiency is the fraction of tracks found by the
track reconstruction code
Efficiency depends strongly on what is counted in the
efficiency “denominator”
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Given the high occupancies and small number of tracking layers, not
all tracks will be findable with high efficiency and low fake rate
Focus on prompt tracks with pT >1 GeV, |h| < 2.5, and |d0| < 2 mm
Take efficiency to be the fraction of selected tracks that are found
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Tracking Efficiency for Muons
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Add randomly distributed muons to pileup interactions
5 GeV muons
100 GeV muons
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Inclusive Fake Track Measurement
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Ideally, the number of reconstructed tracks should scale
linearly with the number of pileup events
An excess of reconstructed tracks is an indication that
fake tracks are being found
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Combinatoric Fakes
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Can also observe fake tracks by looking at MC “truth”
Combinatoric Fakes
1 Hit Mis-assigned
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Purity is the fraction of correctly assigned hits
Tracking Efficiency in High pT Jets
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Tracking efficiency vs DR from jet axis
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Two jet events with pT > 500 GeV, no pileup
e=0.9
DR=0.01
DR
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Fake Track Rate in High pT Jets
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Jets by themselves do not generate fake tracks
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Two jet events with pT > 500 GeV, no pileup
1 Hit Mis-assigned
Purity (fraction of correctly assigned hits)
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Effect of Pileup on Impact Parameter
Z-impact w.r. MC vertex of tracks in jet
2ev pileup
50ev pileup
100ev pileup
150ev pileup
R-impact w.r. MC vertex of tracks in jet
2ev pileup
50ev pileup
100ev pileup
150ev pileup
Fast increase in number of tracks inside jet with big Z impact (pileup)
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Summary
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Simulation studies are crucial to having confidence in
the tracker design as we enter a new regime in terms of
hit density and small number of tracking layers
Upgrade Simulation working group is actively engaged
Several efforts using different tools allow us to take
advantage of unique strengths of the different tools and
provide cross checks
Preliminary results are starting to come in
Current focus is on developing common performance
plots using each tool for a strawman tracker geometry
Some promising results – situation is not hopeless!
Optimization studies will follow once strawman
performance baseline is established
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