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L-Tracker Progress & Plans

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  1. L-Tracker Progress & Plans J.Popp University of California, Irvine Brookhaven National Lab MECO Collaboration Meeting May 14-15, 2004

  2. Outline • Motivation • Cursory test of likelihood fitter – by itself • No event selection (trigger energy cut, pitch angle cut, etc.) • No helix/detector crossing pre-selection for helix recognition • Example from study of finite efficiency and acceptance of straws • Mostly resolvable cluster type ambiguity • Multiple parents for clusters • Sketch of how we do cluster recognition • State of our L-tracker software tools J.Popp, UCI L-Tracker Progress'

  3. Motivation • Goal: Obtain sufficient information about the L-tracker to make an acceptable performance comparison to the T-tracker. • Desirable select detector design SOON: say, six months or less(?) • Acceptance of likelihood fitter must be understood. • Assuming a favorable outcome: we must raise the level of sophistication of L-tracker simulation • Simulate detector operation at the raw data level (TDC times, etc.) • Finish cluster recognition & reconstruction: inefficiencies, etc. • Expand helix recognition pre-selection methods • Fully utilize cluster information: local angle measurements • Add more event types beyond two orbits with (3,3) and (4,4) crossings J.Popp, UCI L-Tracker Progress'

  4. Simulation: GMC Version 077 • Detector solenoid simulation • 10,000 conversion events – higher statistics, later • Electron energy = 105 MeV in stopping target • Magnetic field: MIT design calculation • Calorimeter: crystal • Tracker: • 2848 straws • Wall thickness = 0.0025 cm • Outer radius = 0.25 cm • Length = 260 cm • Modules are tilted to avoid helix crossing a straw more than once • Test TJ’s maximum likelihood fitter – by itself J.Popp, UCI L-Tracker Progress'

  5. Energy Spectra – Smearing Off – 10000 Events • MECO memo 125, Fig. 2 • Entries 18.2 vs 21.9% • Mean 0.283 vs 0.303 MeV • RMS 0.354 vs 0.370 MeV • Chi^2/ndf 283/25 vs 270/127 • Mean 0.327 vs 0.329 MeV • Sigma 0.230 vs 0.209 MeV • MECO memo 125, Fig. 4 • Entries 18.2 vs 21.9% • Mean 104.0 vs 104.0 MeV • RMS 0.681 vs 0.695 MeV • Chi^2/ndf 30.4/27 vs 423/166 • Mean 104.4 vs 104.1 MeV • Sigma 0.329 vs 0.568 MeV J.Popp, UCI L-Tracker Progress'

  6. Energy Spectra – Smearing On – 3500 Events Smearing off vs on: • E(fitter) – E(entry to tracker) • Entries 21.9 vs 19.5% • Mean 0.303 vs 0.310 MeV • RMS 0.370 vs 0.336 MeV • Chi^2/ndf 270/127 vs 85.5/80 • Mean 0.329 vs 0.324 MeV • Sigma 0.209 vs 0.248 MeV • E(fitter) energy spectrum • Entries 18.2 vs 19.5% • Mean 104.0 vs 104.1 MeV • RMS 0.695 vs 0.629 MeV • Chi^2/ndf 423/166 vs 201/139 • Mean 104.1 vs 104.3 MeV • Sigma 0.568 vs 0.545 MeV J.Popp, UCI L-Tracker Progress'

  7. What’s missing Pre-selection cuts in MECO memo 125 make little difference…? • At least 6 helix/detector crossings • Trigger energy > 75 MeV • Pitch angle cut: 45-60 degrees • Likelihood > 0.001 • Fitting uncertainty < 600 MeV • Scattering angle at crossing < 0.08 radians J.Popp, UCI L-Tracker Progress'

  8. Example: Finite Acceptance & 1-hit Inefficiency • Finite Acceptance: • Close-packed tubes, finite thickness • Tube geometry distortion – later • Finite single-tube efficiency: • Roy Lee’s dissertation, p.38: E871 average efficiency = 0.96 • Given an N-hit cluster the probability there are n hits missing is binomially distributed. • Our software can be easily generalized to distinguish clusters with any number of missing hits that we may require. J.Popp, UCI L-Tracker Progress'

  9. Tube did not fire Tube did not fire Resolvable Cluster Type Ambiguity: Example J.Popp, UCI L-Tracker Progress'

  10. Multiple Parents for Distinct Cluster Types • Distinct cluster type, i.e.. geometry • Not a problem, just feature of terrain • Resolved by nonlinear fits to the observed set of time and wire positions • Could also test each hypothesis J.Popp, UCI L-Tracker Progress'

  11. No event selection Example: Ideal Cluster Recognition • Distribution of cluster candidates per event, before reconstruction • Form list of straw hits • Form groups of contiguous hits • Give each hit a group label • Require hits in all 3 layers • Finite acceptances and efficiencies will require us to relax these conditions later • Form all combinations of 3- to 9-hits • Maximum distance between hits • TDC times have maximum separation • Reject combinations with no c-type • Reconstruct each cluster (DTD) • Reject if no tracks returned by cluster reconstruction subroutine J.Popp, UCI L-Tracker Progress'

  12. State of L-Tracker Tool Development • Helix pre-selection methods have been developed at UCI & NYU • Maximum likelihood fitter acceptance is being examined • Could take up to a week, depending on what we find • Finite cluster efficiencies and acceptances: 60-70% complete • DTD: reconstruction underway • FDT: straight-forward once time differences are done • DTD could be completed in 1-2 weeks/ FDT should take a week • Cluster recognition: 50% complete • Test effectiveness of cluster reconstruction on eliminating candidates • Cluster recognition may take 2-4 weeks, possibly less. • Pre-selection for helix recognition is most difficult task • TJ’s FORTRAN is very hard to read and follow • Maybe faster to use NYU pre-selection code for L-tracker. J.Popp, UCI L-Tracker Progress'

  13. Old Pattern Recognition Plan • Previous detector simulation plan: Jan. 2002 • Simulate raw detector signals • Straw clusters: time differences • Pad clusters: time differences • Crystals • Cluster recognition: • Straws: local position & direction in plane perpendicular to wires • Pads: local position along wires • Crystals: local position on sensitive surface • Timing information in all cases • Helix recognition: global track finding • Detector information pre-selection • Maximum likelihood method helix fitter J.Popp, UCI L-Tracker Progress'

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  26. Event Selection • 1000 muon-electron conversions • Crystal calorimeter: E > 85 MeV • Helix pitch angle: 45-60 degrees • Ideal clusters with 3 – 9 hits • Trajectory crosses all 3 planes • Hits in cluster are contiguous • Events with 6-9 clusters J.Popp, UCI L-Tracker Progress'

  27. Multiplicity: Ideal Clusters J.Popp, UCI L-Tracker Progress'

  28. J.Popp, UCI L-Tracker Progress'