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CC analysis update

CC analysis update. D.A. Petyt Sep 1 st 2004. Repeat of CC analysis with R1.9 ntuples What is the effect of improved tracking efficiency? Alternative PID methods: likelihood vs neural net Gallery of events passing/failing PID cuts. Effect of improved tracking in R1.9. R1.9. R1.7.

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CC analysis update

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  1. CC analysis update D.A. Petyt Sep 1st 2004 • Repeat of CC analysis with R1.9 ntuples • What is the effect of improved tracking efficiency? • Alternative PID methods: likelihood vs neural net • Gallery of events passing/failing PID cuts

  2. Effect of improved tracking in R1.9 R1.9 R1.7 R1.9 tracking better; CC selection harder (more high-y events passing cuts)

  3. Effect of improved tracking - pmu R1.9 R1.7 R1.9 tracking improvements obvious in top-right plot

  4. R1.9 reco/selection effics for QEL/RES/DIS

  5. PID performance CC NC Cut at –0.4: 85% CC efficiency, 93% NC rejection

  6. Energy resolution Showers in NC events Showers in CC events Eshw=shw.ph.GeV[0]/1.23 range

  7. Visible energy distributions CC NC reco true Positive bias in CC plot

  8. Comparison of old and new 5 year plan analysis

  9. Comparison of old and new R1.7 analysis

  10. Comparison of old and new R1.9 analysis

  11. PID: comparing techniques • Looked at neural net class in ROOT (TMultiLayerPerceptron) to see how it compares with likelihood technique for separating CC and NC events • Used same variables (event length, track pulse height fraction, track ph/plane) as likelihood analysis. Only used events with evlength<50 planes (events longer than this were assumed to be CC-like) • Advantages of NN: • Correlations between variables accounted for • No binning problems • Advantages of Likelihood method: • Simplicity, transparency

  12. Comparison of PID parameters CC NC Using re-defined PID parameter: PID=p_mu/(p_mu+p_nc) Trained NN outputs a weight: ~0 for NC events, ~1 for CC

  13. Comparison of PID performance NN does better overall – thick red curve higher than black curve. Presumably this is because correlations between variables are taken into account Likelihood seems better for low E events – not entirely sure why this is at the moment… Red: NN, Black: likelihood Thick: all events, Thin: E_nu<3 GeV

  14. CC events passing cuts Cut is PID_lik>0.95

  15. m+p+p0

  16. m+n+p++p0

  17. m+p

  18. NC events passing cuts

  19. n+p++p0

  20. 2p- + n + p0

  21. Classified NC by NN 49 planes long

  22. Long NC event passing 50 plane cut

  23. 5 p in FS – leading pp~ 5 GeV

  24. CC events failing cuts

  25. m + 1 GeV p+ + p+ + p- + n

  26. m + n + p+

  27. m + n + 2p+ +p-

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