1. Upgrade Tracker Simulation Studies
Richard Partridge – SLAC
SLUO LHC Workshop

2. 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

3. 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

4. Simulation Tools Athena / Geant 4 ATLSIM / Geant 3 LCSim / 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

5. 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

6. 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
Moraes et al, EPJ C 50, 435 (2007)
Number of charged particles per interaction (dN/dh) also has some uncertainty

7. 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)
No pT Cut
Diffractives: dN/dh = 0.3 / Int.

8. 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
Long Strips
Pixels

9. Secondary Interactions
Each tracker layer contributes 2-3% X0 of material
Origin of non-prompt charged particles shows substantial contribution from secondary interactions

10. 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, |h| < 2.5, and |d0| < 2 mm
Take efficiency to be the fraction of selected tracks that are found

11. Tracking Efficiency for Muons
Add randomly distributed muons to pileup interactions
5 GeV muons
100 GeV muons

12. 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

13. Combinatoric Fakes
Can also observe fake tracks by looking at MC “truth”
Combinatoric Fakes
1 Hit Mis-assigned
Purity is the fraction of correctly assigned hits

14. Tracking Efficiency in High pT Jets
Tracking efficiency vs DR from jet axis
Two jet events with pT > 500 GeV, no pileup
e=0.9
DR=0.01 DR

15. 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)

16. 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)

17. 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