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, muons and 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

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

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Results• Kalman filter (mu plus, averaged)

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Results• Kalman filter (mu plus, station 1)

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Results• Kalman filter (mu plus, station 1)

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

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

400k spills, different emittance)

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Results• Kalman filter (mu minus)

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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?