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

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, muons and pions, kalman filter 2

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) 3

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

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

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

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

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

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

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

11. Results Kalman filter (mu minus) 11

12. Results Kalman filter (mu minus) 12

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? 13