1. 1 CC analysis update 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 D.A. Petyt Sep 1 st 2004

2. 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. 3 Effect of improved tracking - pmu R1.9 R1.7 R1.9 tracking improvements obvious in top-right plot

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

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

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

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

8. 8 Comparison of old and new 5 year plan analysis

9. 9 Comparison of old and new R1.7 analysis

10. 10 Comparison of old and new R1.9 analysis

11. 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. 12 Comparison of PID parameters CC NC Trained NN outputs a weight: ~0 for NC events, ~1 for CC Using re-defined PID parameter: PID=p_mu/(p_mu+p_nc)

13. 13 Comparison of PID performance Red: NN, Black: likelihood Thick: all events, Thin: E_nu<3 GeV 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…

14. 14 CC events passing cuts Cut is PID_lik>0.95

15. 15  p+  

16. 16  n+    

17. 17  p

18. 18 NC events passing cuts

19. 19 n    

20. 20    n +  

21. 21 49 planes long Classified NC by NN

22. 22 Long NC event passing 50 plane cut

23. 23 5  in FS – leading p  ~ 5 GeV

24. 24 CC events failing cuts

25. 25  GeV        n

26. 26  n +  

27. 27  n +     