1. USCMS May 2002Jim Branson 1 E/Gamma and b/Tau PRS (small US effort) (what you should work on when you finish…) US CMS Annual Collaboration Meeting May 2002 FSU Jin Branson

2. USCMS May 2002Jim Branson 2 ElectronPhoton main packages* EgammaAnalysis Modular analyzer and analysis helpers Abstract “writer” support Iteration wrapper and UserCollection support EgammaNotification Notification and flow control EgammaH4Support Hbook CWN “writer” EgammaClusters Basic clustering algorithms Position and energy corrections Isolation and  0 rejection tools

3. USCMS May 2002Jim Branson 3 ElectronPhoton main packages** ClusterTools Endcap-specific reconstruction Preshower clustering Brem recovery algorithms EgammaL1Tools Level1 trigger candidate matching ElectronFromPixels Pixel matching and track seeding algorithm Electron track reconstruction based on pixel seeds EgammaMCTools Generator- and GEANT-level analysis EgammaTracks Tracking setup and helper classes

4. USCMS May 2002Jim Branson 4 e/gamma Development Working with MC

5. USCMS May 2002Jim Branson 5 Basic Calorimeter Software Activities Calorimeter software has been “stable” for a few years. US is involved in upgrade program. There are three areas of activity –improvements of current architecture of Calorimetry FORTRAN elimination using new ROU naming schema navigation and speedup optimization –online/testbeam specific preparations splitup the Readout on two parts to read the online/testbeam format –preparations for the migration to OSCAR isolation of what is required for hit-formatting first prototype of DDD usage

6. USCMS May 2002Jim Branson 6 Island Clustering Fast, reliable bump finding Standalone reconstruction Accurate position reconstruction Log-weighed Position Correction

7. USCMS May 2002Jim Branson 7 Depth modeling Dependence of shower max on energy ~log(E) with energy in GeV T max = A[T 0 +log(E)] Parameterization for ECAL with A = 0.89 (PbO 4 rad length) Optimize T 0 by finding the zero offset for the two half barrels (optionally one could minimize position resolution) Specific for electrons OR photons

8. USCMS May 2002Jim Branson 8 Log weighting Linear-weighted cog produces characteristic s-shape Rather than applying ad-hoc correction, use a log weight: W 0 ~ smallest fractional energy to contribute to position calculation Linear weight Log weight W 0 =4.2

9. USCMS May 2002Jim Branson 9 Position resolution

10. USCMS May 2002Jim Branson 10 Brem recovery Average brem loss (~44%) corresponds to an average thickness of 0.57 X 0 Need a brem recovery strategy in ECAL Cluster composite ECAL objects according to some criterion –E.g. energy deposition from brem well aligned in  Use narrow  window Collect clusters along  Produces a SuperCluster – collection of ECAL clusters Removes large tails

11. USCMS May 2002Jim Branson 11 Hybrid algorithm Use  geometry of barrel crystals –Start from a seed crystal (as for island) –Take a fixed domino of 3 or 5 crsytals in  –Search dynamically in  In more detail: –Start if E t seed >E t hyb –Make 1x3 domino –If center of domino>E wing Extend to 1x5 –Proceed N step in 5 –Remove dominoes below E thresh –Disconnected domino preclusters with E>E seed are then reclustered in  (producing a SuperCluster)

12. USCMS May 2002Jim Branson 12 Optimize Hybrid Performance

13. USCMS May 2002Jim Branson 13 Energy Scale Energy is estimated by the sum of energy deposits Emeas/Etrue gaussian+tail, peaking at <1 –Incomplete containment –Unrecovered brem Set the energy scale such that the gaussian peak falls at 1 –Parameterize corrections as a function of the number of crystals included in the cluster –E.g. for hybrid (barrel) clusters Electrons 10-50 GeV

14. USCMS May 2002Jim Branson 14 Energy scale performance I In the barrel, with hybrid clusters: –No P t dependence –Small residual  dependence

15. USCMS May 2002Jim Branson 15 Energy resolution Effective width is defined as the half-width containing 68.3% of the distribution Performance on unconverted photons (using fixed window): –  eff /E ~ 0.9 %

16. USCMS May 2002Jim Branson 16 Preshower matching Endcap SuperCluster extrapolate components to Preshower search PS cluster in narrow road around extrapolated point correct component energy Recalc SuperCluster energy

17. USCMS May 2002Jim Branson 17 Pixel Matching (level “2.5”)

18. USCMS May 2002Jim Branson 18 e/  Level 2.5

19. USCMS May 2002Jim Branson 19 e/  Triggers

20. USCMS May 2002Jim Branson 20 Pixel “tracklet” Cluster Electron Tracks Use “standard” tracking with pixel seeds from matching “Level 2” clusters –Fast (few tracks to reconstruct) –In the spirit of “regional” reconstruction Special e track fitter may help.

21. USCMS May 2002Jim Branson 21 Electron Position Matching in 

22. USCMS May 2002Jim Branson 22 Electron Rates and Efficiency

23. USCMS May 2002Jim Branson 23 HLT Algorithm Timing Time on (dual) 700 MHz P III Data access time (objectivity) excluded Optimization possible.

24. USCMS May 2002Jim Branson 24 June Milestones

25. USCMS May 2002Jim Branson 25 Tracking Photon Conversions Efficiency still low due to seeds

26. USCMS May 2002Jim Branson 26 Callibration with W  e Callibration with W  e

27. USCMS May 2002Jim Branson 27 Background to  after standard cuts plus tracker and ecal isolation

28. USCMS May 2002Jim Branson 28 Egamma/Jet Available

29. USCMS May 2002Jim Branson 29 b/  (Tracker Group) Many developers and much progress. US not involved (?). Software depends on CommonDet.

30. USCMS May 2002Jim Branson 30 ORCA for the Tracker 4 subsystems: everything up to the persistent digisTracker: geometry, hit formatting, hit loading, digitization and persistency. Let’s say: everything up to the persistent digis. This is the package which has to be ready for the Monte Carlo productions. TrackerReco: anything which has to do with reconstructed objects: RecHits and Tracks. In principle those are not persistent, even if now tracks can be written to DB. Vertex: same as above, but dealing with primary and secondary vertices. bTauAnalysis: high level objects, like b and tau taggers. They use all the above packages. Tracker TrackerReco Vertex bTauAnalysis

31. USCMS May 2002Jim Branson 31 Tracker Geometry: put some detectors in the space and call it a Tracker Hit Formatting: cmsim flat file to Persistent DB structure Hit Loading: read back the last Digitizing: simulate the electronics attached to the sensors, and apply filters to reduce the data volume.

32. USCMS May 2002Jim Branson 32 Geometry The number of hits a charged track can leave is always > 10, considered enough to allow an efficient tracking and a reasonable combinatorial overhead. Number of Si hits excluding pixels

33. USCMS May 2002Jim Branson 33 Digitization New and more reliable (from real tests in Karlsruhe) treatment of the Lorentz angle in silicon, as a function of bias, irradiation etc. Not yet implemented for pixels, where the modeling is more difficult (after irradiation, the depletion will not be complete…); wait for the optimization workshop Code in ORCA can be adapted via configurables to any Irradiation conditions Temperature V bias Etc… Lorentz angle very important for hit resolution: Silicon: tan(  L ) = 0.12 (~6° at 4T) Pixel: tan (  L ) = 0.53 (~28° at 4T) Silicon

34. USCMS May 2002Jim Branson 34 RecHit Resolution Versus r Versus z Mean error RMS

35. USCMS May 2002Jim Branson 35 Seed Generation In this step a first approximation of a track is constructed using some supposed clean information. You can think about different types of seeds: Take any two silicon/pixel layers and fit a helix with each pair of hits fulfilling some conditions Use the 2/3 pixel layers Have a seed from outside (for example muons + beam spot or calorimeters) Seed generation affects efficiency and timing greatly.

36. USCMS May 2002Jim Branson 36 Available Seed Generators Currently available: CombinatorialSeedGeneratorFromPixel: the standard one SeedFromConsecutiveHits: takes 2 consecutive layers and uses the hits to build a seed SeedFromSeparatedHits: even more difficult! SeedGeneratorFromSimTrack: a MC based seed generator with 100% efficiency. Useful for tests.

37. USCMS May 2002Jim Branson 37 Pixel Inefficiencies Different staging/Lumi scenarios L = 2  10 33 L = 10 34 Expected Inefficiencies at 1/2/10  10 34

38. USCMS May 2002Jim Branson 38 Seeding with Pixel + Silicon Hence, work has started to produce seeds from pixels + the first layer of microstrips. Remember that it is 20 cm away from the IP, so you expect a huge number of compatible RecHits and thus a combinatorial explosion.

39. USCMS May 2002Jim Branson 39 Seeds from Pixel + Silicon

40. USCMS May 2002Jim Branson 40 New Propagator AnalyticalPropagator: a new implementation in ORCA 6. Better protected against numerical problems, more precise and as fast as the Gtf. TO BECOME THE STANDARD SOON!!!!

41. USCMS May 2002Jim Branson 41 Trajectory Cleaning Since the generation of trajectories from the seeds is not one-to- one, we can in the end have two or more different trajectories sharing a great fraction of the hits and thus are not compatible. Such ambiguities are resolved by the trajectory cleaner, which identifies mutually exclusive subsets and chooses one trajectory per subset. It works by iterating over the input trajectories, finding for each Trajectory all the others which share more than a given number of hits with it, and then choosing the best trajectory in the set, where best is based on the chi2 of the fit.

42. USCMS May 2002Jim Branson 42 Trajectory Smoothing Since the trajectory building starts with a seed, typically close to the beam spot, and propagates to the outer barrel. In this way, the last fit is done when reaching the end and there all the information is available. Close to the start, where (by the way!) we are usually more interested in the track parameters, we have initial information. A smoothing algorithm guarantees an uniform and optimal set of parameters everywhere. In this stage, no new hits are allowed, but some hits might be dropped if found not compatible wrt to the full information.

43. USCMS May 2002Jim Branson 43 Performances No 2-pixels!

44. USCMS May 2002Jim Branson 44 Track Parameter Resolution

45. USCMS May 2002Jim Branson 45 OFFLINE HLT B tagging in HLT We can trigger on b- jets on the online farm with performances similar to those we obtain offline!

46. USCMS May 2002Jim Branson 46Timing Pixel Readout: PixelReconstruction::doIt Seed Generator: PixelSelectiveSeeds::seeds [< 5%] Trajectory Builder: CombinatorialTrajectoryBuilder::trajectories [>80%] Trajectory Smoother: KalmanTrajectorySmoother::trajectories [<10%] Trajectory Cleaner: TrajectoryCleanerBySharedHits::clean [~ 1%] Trajectory Builder: CombinatorialTrajectoryBuilder [ModularKFReconstructor::reco] Tagging: BTaggingAlgorithmByTrackCounting::isB

47. USCMS May 2002Jim Branson 47Timing

48. USCMS May 2002Jim Branson 48 Tracker Material

49. USCMS May 2002Jim Branson 49 Detailed Description

50. USCMS May 2002Jim Branson 50 Pixel Geometry

51. USCMS May 2002Jim Branson 51 Total Tracker Material

52. USCMS May 2002Jim Branson 52 Total Tracker Absorption Lengths

53. USCMS May 2002Jim Branson 53 Alignment Studies Alignment Tools: they work –one can still add functionality Mis-Alignment studies: –reconstruction is uncritical up to even 1mm/1mrad misalignment (10 times more than survey/laser-alignment accuracy) Trigger ? update documentation (done), Note (preparation)

54. USCMS May 2002Jim Branson 54Summary CMS is making good progress on software and HLT studies in both e/gamma and b/tau. Current production to meet June milestones: still ambitious, US role in these groups is small so far.