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This report revisits the CC analysis utilizing the R1.9ntuples to investigate the effects of improved tracking efficiency on particle identification (PID) methods. We compare likelihood and neural network approaches for distinguishing between charged current (CC) and neutral current (NC) events, examining performance with new and historical analysis data. The efficiency improvements in tracking yield significant insights into high-y event selections and energy resolutions. The outcomes reveal a bias in CC event recognition and demonstrate the neural network's advantages in PID performance over likelihood methods.
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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
Effect of improved tracking in R1.9 R1.9 R1.7 R1.9 tracking better; CC selection harder (more high-y events passing cuts)
Effect of improved tracking - pmu R1.9 R1.7 R1.9 tracking improvements obvious in top-right plot
PID performance CC NC Cut at –0.4: 85% CC efficiency, 93% NC rejection
Energy resolution Showers in NC events Showers in CC events Eshw=shw.ph.GeV[0]/1.23 range
Visible energy distributions CC NC reco true Positive bias in CC plot
Comparison of old and new 5 year plan analysis
Comparison of old and new R1.7 analysis
Comparison of old and new R1.9 analysis
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
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
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
CC events passing cuts Cut is PID_lik>0.95
Classified NC by NN 49 planes long
