Jim Branson FSU High Energy Physics

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Jim Branson FSU High Energy Physics Powered By Docstoc
					E/Gamma and b/Tau PRS
           (small US effort)
   (what you should work on when you finish…)

 US CMS Annual Collaboration Meeting May 2002

               Jin Branson

                                                Jim Branson   1
          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
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        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
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e/gamma Development

Working with MC

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         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

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                 Island Clustering
Fast, reliable
bump finding

            Accurate position   Log-weighed Position Correction

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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
      •Optimize T0 by finding the
      zero offset for the two half
      barrels (optionally one
      could minimize position
      •Specific for electrons OR

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                Log weighting
                    •    Linear-weighted cog produces
Linear weight            characteristic s-shape
                    •    Rather than applying ad-hoc
                         correction, use a log weight:

                         W0 ~ smallest fractional energy
                        to contribute to position calculation

                   Log weight W0=4.2

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Position resolution

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

                                         •   Produces a SuperCluster –
                                             collection of ECAL
                                         •   Removes large tails

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                          Hybrid algorithm
                                     •   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
• Use h-f geometry of barrel              – Disconnected domino
  crystals                                  preclusters with
   – Start from a seed crystal (as          E>Eseedare then
     for island)                            reclustered in f
   – Take a fixed domino of 3 or 5          (producing a
     crsytals in h                          SuperCluster)
   – Search dynamically in f

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Optimize Hybrid Performance

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                                 Energy Scale
•Energy is estimated by the sum of
energy deposits
                                        Electrons 10-50 GeV
•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

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

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

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Preshower matching

    •   Endcap SuperCluster
    •   extrapolate components to
    •   search PS cluster in narrow road
        around extrapolated point
    •   correct component energy
    •   Recalc SuperCluster energy

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Pixel Matching (level “2.5”)

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e/g Level 2.5

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e/g Triggers

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                            Electron Tracks

• Use “standard” tracking
  with pixel seeds from
  matching “Level 2”
  – Fast (few tracks to
  – In the spirit of “regional”
                                  Pixel “tracklet”
• Special e track fitter
  may help.

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Electron Position Matching in h

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Electron Rates and Efficiency

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

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June Milestones

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Tracking Photon Conversions

   Efficiency still low due to seeds
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Callibration with W®en

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Background to H®gg after standard cuts plus
        tracker and ecal isolation

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Egamma/Jet Available

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

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                     ORCA for the Tracker

•4 subsystems:
   •Tracker: geometry, hit formatting, hit loading,       Tracker
   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
   •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
   •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.            bTauAnalysis

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•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.

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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
                                            Jim Branson   32

•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

                                                   Code in ORCA can be
  Lorentz angle very important for hit             adapted via configurables
                                                   to any
                                                   •Irradiation conditions
  •Silicon: tan(qL) = 0.12 (~6° at 4T)             •Temperature              n
  •Pixel: tan (qL) = 0.53 (~28° at 4T)
                                                   •V bias
                                                                    S ili

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          RecHit Resolution

   Versus r             Versus z
Mean error


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                       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
•Seed generation affects efficiency and timing greatly.

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               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.

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                       Pixel Inefficiencies

Different staging/Lumi scenarios

         L = 2´1033                 L = 1034

   Inefficiencies at
    1/2/10 ´ 1034

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          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

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Seeds from Pixel + Silicon

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                  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

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                    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.
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                   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.

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No 2-pixels!

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Track Parameter Resolution

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                          B tagging in HLT

We can trigger on b-
jets on the online farm
with performances
similar to those we
obtain offline!



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•   • 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

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         Jim Branson   47
Tracker Material

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Detailed Description

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Pixel Geometry

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Total Tracker Material

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Total Tracker Absorption Lengths

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                   Alignment Studies
• Alignment Tools: they work ü
  – 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)

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• CMS is making good progress on software and
  HLT studies in both e/gamma and b/tau.
• Current production to meet June milestones: still
• US role in these groups is small so far.

                                          Jim Branson   54

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