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Tracking Efficiency and Momentum Resolution Analysis

Tracking Efficiency and Momentum Resolution Analysis. Chris Meyer UCSC ILC Simulation Reconstruction Meeting March 13, 2007. Outline. Analyze the efficiency of working track finder Determine limits of reconstruction efficiency in momentum, radius of origin etc…

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Tracking Efficiency and Momentum Resolution Analysis

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  1. Tracking Efficiency and Momentum Resolution Analysis Chris Meyer UCSC ILC Simulation Reconstruction Meeting March 13, 2007

  2. Outline • Analyze the efficiency of working track finder • Determine limits of reconstruction efficiency in momentum, radius of origin etc… • Explore performance of track fitter (for now simple helix fitter to demonstrate analysis capabilities)

  3. Definition of findable track (filters) • Cos() < 0.5 • rOrg < 400 mm • Pathlength < 500mm • Final or Intermediate State Particle • Purity of hits on track > 0.79 from same MC Particle Also: Events only accepted if cos(T) < 0.5 and thrust value > 0.94 Criteria developed by Bruce Schumm, Lori Stevens, and Tyler Rice after some study

  4. SODTrack Analyzer • SODTrack reconstruction written by Fred Blanc; extends VXD stubs into central tracker. For now uses cheating for VXD stubs. Fitter: simple helix fit? • Original tracking efficiency code written by Eric Wallace (UCSC Undergraduate, now at U of W) was implemented to create root output files • The sid01 files were used for ZPole bbbar events, and sigaug05 for 500 GeV uds events. Now let’s check out the baseline efficiency…

  5. Total Efficiency of 500 GeV uds At first look 93%, not great Found Tracks Missed Tracks

  6. Possible Sources of Inefficiency? • Low momentum tracks are included in denominator; explore a High Transverse momentum filter? • Track originates outside vertex detector? • Confusion in the core of the jet? We know SODTracker will be inefficient for tracks that don’t start at the origin…

  7. ZPole bbbar All tracks originating outside 1.4 cm aren’t found…

  8. Prompt/Non-Prompt Tracks From here on out we’ll also require that the tracks originate within 1 mm of the origin Note: Tyler Rice and Lori Stevens are working on optimizing Tim Nelson’s AxialBarrelTracker to find non-prompt tracks, currently getting > 50% efficiency assuming no z-segmentation (Lori in process of adding z-segmentation)

  9. Total Efficiency for Prompt Tracks 500 GeV uds Making a pT > 5 GeV cut increases efficiency about 0.3% 98.46% efficiency for pT >.75 GeV/c 98.77% efficiency for pT > 5 GeV/c

  10. Efficiency vs pT-1 500 GeV uds At higher pT-1 (lower pT) there is some inefficiency

  11. Fake Rates vs. Momentum Calculated from all SODTrack hits; “fake” is < 0.80 hits from same MC Particle.

  12. Additional Inefficiencies? Are there additional inefficiencies caused by the dense jets? Define  as the angular difference between the candidate MC track and event thrust axis

  13. Efficiency vs.  500 GeV uds No Apparent effect as you go into the jet core (small ) pT > 0.75 GeV pT > 5.0 GeV

  14. Tracking Efficiency Conclusions • Inefficient at finding tracks outside 1-2 cm • 98.5-99% efficiency for tracks within 1 mm (but relies on cheating for VXD stubs) • Fake rates around 1% except for lower momentum • Some dependence on pT-1, somewhat better at high momentum • No discernable dependence on angle from jet core Now let’s take a look at the momentum resolution

  15. But what’s the curvature error on these measurements? Let’s form a plot of three different errors: • Take the Gaussian variable p - t , perform a Gaussian fit to find  for 10 different ranges. • Inside each range the values still come from a different distributions, so add 2 from the tracker in quadrature and take the average to find error expected by track finder. • Calculate the optimal expected error given systemic properties using LCDTRK (developed by Bruce Schumm, UC Santa Cruz). This gives us a “triplot” of error.

  16. Calculating error from residuals Example of a Gaussian fit for 0.000375 <  < 0.0002

  17. Calculating error from residuals Example of a Gaussian fit for 0.015 <  < 0.0075

  18. The Triplot, 500 GeV uds

  19. Triplot Conclusions At this point we are ready to test the new Kalman Filter Fitter from Fred as well as Norman’s new package. To increase the precision of our study we need to: • Restrict to tracks with hit on all layers • Do a more precise averaging of the LCDTRK predictions within each bin. This should be ready within a week.

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