Fast Pattern Recognition for CMS Track Finding

1. Malina Kirn 9/28/2006 Project Proposal, AMSC 663 Advisor: Dr. Nicholas Hadley Fast Pattern Recognition for CMS Track Finding

2. Goal: Improve the running time of an existing track finding software package designed for the CMS experiment while preserving physics performance. Science, CMS, Tracker Track finding algorithm Project Outline

3. Higgs Boson New Physics Dark Matter Science Goals

9. Si strip detectors Tracking Detectors Pixels

10. Problem Size Pixels x 66 million 100 μm 150 μm Stripsx 9.6 million 10-25 cm • Final data rate of 150 Hz • 1,000-10,000 hits/event 80-180 μm

11. double sided strip sensor (stereo) single sided strip sensor TOB r TIB TID TEC z

12. Goal: reconstruct tracks from hits Use roads to reduce the problem space The RoadSearch Algorithm

13. Create seeds & trajectories Seed = (ideally) the end points of a track Trajectory = first guess track Create clouds Cloud = clustering of hits around trajectory Clean/merge clouds Find track candidates Create tracks The RoadSearch Algorithm

14. Roads

15. outer Rings for RoadSeeds inner Rings for RoadSeeds r z

16. Match pairs of hits in the inner and outer rings of a single road. Create seed if pair satisfies Δφcut. Create trajectory using the three points of beam spot and inner/outer hits. One real track can have multiple seeds. One event typically contains multiple seeds and trajectories. Goal: identify tracks 1) Create seeds & trajectories

17. 1) Create seeds & trajectories r-z plane r-φ plane

18. Loop over rings in road from inside→outside. Include hits in a Δφ window, which gets larger with r. Cloud contains one trajectory and multiple hits. Goal: identify hits associated with track 2) Create clouds

19. Can have multiple clouds/track because of possible multiple seeds/track A single track described by multiple clouds is bad Merge clouds which share ≥70% of their hits. Create new trajectory based on merged hits. Cloud contains one trajectory and multiple hits. Goal: one cloud per track 3) Clean/merge clouds

20. Create one track candidate/cloud. Start with trajectory. Add hits from inside→out if χ2 from new hit is less than cut. Update the track candidate. Pass a cloud to (5) with the track candidate and only good hits. Goal: keep only best hits, create initial track 4) Find track candidates

21. Given track candidate + good hits, create one track. Iteratively apply Kalman filter on all hits. Goal: final track creation 5) Create tracks

22. Tracks!

23. Final data rate of 150 Hz Event reconstruction at CERN farm (order 1000 cores) must be ≥ 150 Hz CMS outputs ~5 PB/year Roadsearch is currently too slow Track finding consumes ~50% of time Plan algorithmic and technical improvements Project

24. Project: Platform • CMS uses several grids for data distribution • Must be able to test and run at any grid site • Software installed at all grid sites • Constrained to run within software • Cannot make hardware-specific improvements • I will develop at Fermilab

25. Project: Test Problem • Monte Carlo generated events • Use MC single and multiple track events to measure success rate and running time • Speed improvements desirable for all event types • Must know speed & success rate before & after changes • Comparison to other tracking algorithm desirable

26. Summary • Roadsearch is a new track finding algorithm with unique features • Goal is to improve Roadsearch running time • Grid puts constraints on possible solutions • Testing done on well-understood datasets