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Introduction

Introduction. Miha Zgubič, summer student Scintillating fibre tracker software Analysis of performance of momentum reconstruction. What has been done?. Compare MC truth to reconstructed values (longitudinal and transverse momentum, pz&pt ) – both PR and kalman

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Introduction

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Presentation Transcript

  1. Introduction • Miha Zgubič, summer student • Scintillating fibre tracker software • Analysis of performance of momentum reconstruction

  2. What has been done? • Compare MC truth to reconstructed values (longitudinal and transverse momentum, pz&pt) – both PR and kalman • Call the width of Gaussian “resolution” Resolution plotted as a function of pz or pt. (histograms fitted separately for each MC momentum interval) noise, muonsand pions, kalman filter

  3. Details • Lookup table between MC and recon side • Beam: • 10k spills at 200MeV • Emittance of 6.0 • Cut on reconstructed pz and pt at 500MeV • Kalman: • Algorithm 1: station 1 recon momentum used (better feel for what is going on) • Algorithm 2: recon momentum values averaged over the trackpoints (better resolutions results)

  4. Results • Pattern recognition (mu plus, others similar) • Low statistics -> large error bars

  5. Results • Noise on/off comparison, PR (mu plus)

  6. Results • Kalman filter (mu plus, averaged)

  7. Results • Kalman filter (mu plus, station 1)

  8. Results • Kalman filter (mu plus, station 1)

  9. Results • Compare kalman and pattern recognition (mu plus, averaged)

  10. Results • Compare kalman and pattern recognition (mu plus, averaged, 400k spills, different emittance)

  11. Results • Kalman filter (mu minus)

  12. Results • Kalman filter (mu minus)

  13. Conclusions • PR works fine • Noise has little impact on performance • Kalman as good as PR for pz– not better • Worse than PR for pt, and sometimes produces very large values of pt • Possibly a bug for negative particles?

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