Upgrade Tracker Simulation Studies

Richard Partridge – SLAC

SLUO LHC Workshop

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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 Challenging environment for track finding

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

Detailed / realistic simulations essential 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 Optimizing the tracker design requires the ability to compare design

alternatives without extensive code changes

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Simulation Tools Athena / Geant 4

Adapt existing ATLAS detector simulation to upgrade geometry New layers overflow 32 bit identifier scheme 64 bit identifiers Need to accommodate new endcap geometry

ATLSIM / Geant 3 Used for early ATLAS simulations Very detailed description of current detector Some geometry changes easy, some are hard

LCSim / Geant 4 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) Fast hit simulation from MC generated particles Material description extracted from Athena / Geant 4

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Why Have Multiple Tools? Cross check / verification of results

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

Different tools have different strengths 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 Spurs innovation, challenges assumptions / prejudices

Some VERY preliminary results follow

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Pileup Interactions Low pT pileup interactions dominate tracker occupancy For L = 1035, ~400 interactions/xing at 50 ns spacing

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

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7) Number of charged particles per interaction (dN/d) also has some uncertainty

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Charged Particle Multiplicity at L = 1035

ATLAS Tune: dN/d = 6.8 / Int

Tevatron Tune: dN/d = 5.6 / Int

(5.3 if you remove diffractives)

Diffractives: dN/d = 0.3 / Int.

No pT Cut

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Tracker Occupancy 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

Pixels

Long Strips

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Secondary Interactions 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 Tracking efficiency is the fraction of tracks found by the

track reconstruction code Efficiency depends strongly on what is counted in the

efficiency “denominator” 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, || < 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 Add randomly distributed muons to pileup interactions

5 GeV muons 100 GeV muons

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Inclusive Fake Track Measurement 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 Can also observe fake tracks by looking at MC “truth”

1 Hit Mis-assignedCombinatoric Fakes

Purity is the fraction of correctly assigned hits

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Tracking Efficiency in High pT Jets

Tracking efficiency vs R from jet axis Two jet events with pT > 500 GeV, no pileup

R

R=0.01

=0.9

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Fake Track Rate in High pT Jets

Jets by themselves do not generate fake tracks 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 ParameterZ-impact w.r. MC vertex of tracks in jet

R-impact w.r. MC vertex of tracks in jet

Fast increase in number of tracks inside jet with big Z impact (pileup)

2ev pileup 50ev pileup 100ev pileup 150ev pileup

2ev pileup 50ev pileup 100ev pileup 150ev pileup

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Summary 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