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Atmospheric Neutrino Event Reconstruction

Atmospheric Neutrino Event Reconstruction. Andy Blake Cambridge University June 2004. Introduction. Reconstruction Track/shower finding Track fitting (fast measurement of track curvature). Testing Run over most of the data. Used in Caius’ analysis. Fast ( ~100 ms / event ).

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Atmospheric Neutrino Event Reconstruction

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  1. Atmospheric Neutrino Event Reconstruction Andy Blake Cambridge University June 2004

  2. Introduction • Reconstruction • Track/shower finding • Track fitting • (fast measurement • of track curvature) • Testing • Run over most of the data. • Used in Caius’ analysis. • Fast ( ~100 ms / event ). AtNuReco • Analysis Modules • Raw Digit Dump. • (raw digits, TPMT hits, dead chips etc…) • Cand Digit Dump. • (positions, times, pulseheights, fibre lengths etc…) • Cand Track/Shower Dump. • (track/shower parameters, analysis variables etc…) • Event Display.

  3. This Talk • Direction Reconstruction → Timing Resolution • Charge Reconstruction → Separating  /  _

  4. Direction Reconstruction

  5. Up-Going Events • Direction-Finding Algorithm: • consider distance vs time for track • force fits with β = ± 1 • calculate RMS about each fit • RMSdown-RMSup> 0 for up-going tracks. UP-GOING EVENT ! up-going neutrinos

  6. MC/data Comparisons

  7. Timing Resolution RMSdown for stopping muons: Timing resolution: Data = 2.75 ns MC = 2.40 ns Try to understand this discrepancy: - refractive index - time walk - timing calibration

  8. Refractive Index Use double-ended strips on muon tracks: t0 muon tp tm Lm Lp nreco=1.75 ndata=1.82 nMC=1.73

  9. Timing Calibration define: measured time difference - expected time difference between strips ends between strip ends RMS Δt measures intrinsic timing resolution Use measured refractive indices Mean Δt tests goodness of calibration

  10. Timing Calibration Poor calibration in last two crates Mean Δt ( → test calibration constants ) Overall calibration good to <0.5 ns September 2003 data ~3000 entries per plane Monte Carlo peaks at -0.3 ns (weird east-west asymmetry)

  11. Timing Calibration RMS Δt ( → measure intrinsic timing resolution ) September 2003 data MC slightly better than data. Using n=1.82 instead of n=1.75 improves resolution by ~0.2 ns.

  12. Time Walk Timing resolution depends on size of signal: RMS Δt (set Qm ≈ Qp)

  13. Time Walk Signal rise time depends on size of signal: … but timing fits are charge-weighted so this effect gets suppressed.

  14. Timing Resolution Try to reproduce tracking resolutions by combining individual errors: use ΔL ~ 4m Total resolution Intrinsic resolution Refractive index Time Walk Calibration Monte Carlo : 2.45 ns 2.4 ns  0.25 ns 0.5 ns  = Data : 2.7 ns 2.45 ns 0.9 ns 0.7 ns 0.3 ns    =

  15. Timing Drift ~5% degradation in 6 months

  16. Timing Drift RMS Δt ( → intrinsic timing resolution ) September 2003 data 2% systematic variation across detector? Timing resolution per plane - SM1 slightly worse than SM2?

  17. Timing Drift Current Timing Calibration: • Overall calibration has degraded (~0.3 ns → ~0.4 ns) • some structure + large displacements have developed.

  18. Current Calibration • Timing structure lines up with VARC + crate boundaries.

  19. Current Calibration • Large displacements are due to hardware changes. • VFB swaps, VARC swaps, dynode threshold adjustments etc…

  20. Up-Going Events 2.5 kT-yrs data PC up-going tracks after timing cuts • Correlations between regions of poor calibration and up-going tracks !

  21. Up-Going Events Up-going candidate: planes 118 – 128 - real or badly calibrated ?

  22. Up-Going Events Up-going candidate: planes 438 – 447 - real or badly calibrated ?

  23. Conclusions • Far Det timing resolution is ~2.5ns - Monte Carlo and data agree to <5%. - Resolution in data degraded by calibration + larger time walk + choice of refractive index. • Timing calibration vital for analysis of up-going atmospheric neutrinos. - Calibration constants are drifting over time. - Hardware changes cause significant shifts. - Need to correct for these changes.

  24. Charge Reconstruction

  25. Charge Reconstruction • Charge-Finding Algorithm: • need to measure curvature of • muon track in magnetic field. • split track into overlapping • 15 plane segments and parametrize • each track segments using quadratic fit. • calculate Q/p and ΔQ/p for each segment. • combine the measurements to give an • overall value for Q/p and ΔQ/p.

  26. Charge Separation

  27. Comparison with SR Compare: SR tracker + SR fitter (total efficiency = 86%) AtNu tracker + AtNu fitter (total efficiency = 89%) (atmos  CC, >10 track planes, passed track fitter) ~20% slightly better charge separation for AtNu ~30%

  28. Comparison with SR FITTING Look at all combinations : TRACKING AtNu provides slightly better tracking for atmos nu events. SR provides slightly better fitting for atmos nu events.

  29. Comparison with SR Charge separation in cosmic muons: AtNu better at lower energies SR better at higher energies

  30. Comparison with SR

  31. Conclusions • AtNu charge reconstruction in good shape. - Algorithm is fast and robust. - Performs well at low energies, → ideal for FC atmospheric neutrino analysis. - Efficiency starts to drop away for E >20 GeV. • Momentum from curvature good to ~30%. - plan to refine assumptions made in algorithm.

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