E/Gamma and b/Tau PRS (small US effort) (what you should work on when you finish…)

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. 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 p0 rejection tools

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. e/gamma Development Working with MC

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. Island Clustering Standalone reconstruction Fast, reliable bump finding Accurate position reconstruction Log-weighed Position Correction

7. Depth modeling • Dependence of shower max on energy ~log(E) with energy in GeV Tmax = A[T0+log(E)] • Parameterization for ECAL with A = 0.89 (PbO4 rad length) • Optimize T0 by finding the zero offset for the two half barrels (optionally one could minimize position resolution) • Specific for electrons OR photons

8. Log weighting • Linear-weighted cog produces characteristic s-shape • Rather than applying ad-hoc correction, use a log weight: Linear weight W0 ~ smallest fractional energy to contribute to position calculation Log weight W0=4.2

9. Position resolution

10. Average brem loss (~44%) corresponds to an average thickness of 0.57 X0 Need a brem recovery strategy in ECAL Cluster composite ECAL objects according to some criterion E.g. energy deposition from brem well aligned in h Use narrow h window Collect clusters alongf Produces a SuperCluster – collection of ECAL clusters Removes large tails Brem recovery

11. Use h-f geometry of barrel crystals Start from a seed crystal (as for island) Take a fixed domino of 3 or 5 crsytals inh Search dynamicallyinf In more detail: Start if Etseed>Ethyb Make 1x3 domino If center of domino>Ewing Extend to 1x5 Proceed Nstep in 5 Remove dominoes below Ethresh Disconnected domino preclusters with E>Eseedare then reclustered in f (producing a SuperCluster) Hybrid algorithm

12. Optimize Hybrid Performance

13. 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 Energy Scale Electrons 10-50 GeV

14. Energy scale performance I • In the barrel, with hybrid clusters: • No Pt dependence • Small residual h dependence

15. Energy resolution • Effective width is defined as the half-width containing 68.3% of the distribution • Performance on unconverted photons (using fixed window): • seff/E ~ 0.9 %

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. Pixel Matching (level “2.5”)

18. e/g Level 2.5

19. e/g Triggers

20. Electron Tracks Cluster Pixel “tracklet” • 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. Electron Position Matching in h

22. Electron Rates and Efficiency

23. HLT Algorithm Timing • Time on (dual) 700 MHz P III • Data access time (objectivity) excluded • Optimization possible.

24. June Milestones

25. Tracking Photon Conversions Efficiency still low due to seeds

26. Callibration with Wen

27. Background to Hgg after standard cuts plus tracker and ecal isolation

28. Egamma/Jet Available

29. b/t (Tracker Group) • Many developers and much progress. • US not involved (?). • Software depends on CommonDet.

30. Tracker TrackerReco Vertex bTauAnalysis ORCA for the Tracker • 4 subsystems: • Tracker: 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.

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. 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. 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. RecHit Resolution Versus r Versus z Mean error RMS

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. 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. Pixel Inefficiencies Different staging/Lumi scenarios L = 21033 L = 1034 Expected Inefficiencies at 1/2/10  1034

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. Seeds from Pixel + Silicon

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. 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. 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. Performances No 2-pixels!

44. Track Parameter Resolution

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. Timing • • 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. Timing

48. Tracker Material

49. Detailed Description

50. Pixel Geometry