1. Update on NC/CC separation At the previous phone meeting I presented a method to separate NC/CC using simple cuts on reconstructed quantities available in eventsr.root. –summarised in the next couple of slides. At low neutrino energy, there is large overlap between CC and NC distributions – likelihood or neural net methods are likely to provide better separation for these events. I have looked into a likelihood-based analysis and present the first results here. Ultimately, new (improved) efficiencies will replace the old reco_minos-derived efficiencies that are currently used in the CC energy sensitivity calculations. D.A. Petyt March ‘03

2. Event length cut efficient for separating NC/CC at high energy (histogram). Addition of ‘track fraction’ cut is an improvement over old reco_minos analysis. Alternative cuts for short events can increase efficiency at low energy (points), at the expense of increased NC background. Limited scope for improvement at low energies – large overlap between CC and NC distributions Cut-based NC/CC separation

3. Comparing old and new efficiencies Histograms – new efficiencies, points – old RECO_MINOS efficiencies

4. Likelihood-based separation Analysis 2: Likelihood based separation of CC and NC. Use distributions as PDFs

5. Constructing the likelihood Calculate the probability that a given event comes from the CC or NC distributions: Calculate a PID parameter (following Super-K) based on the difference in log likelihood between the CC and NC hypotheses CC NC

6. Efficiencies for 2 values of the PID cut likelihood cuts Likelihood analysis with this PID cut seems to be slightly better than the cut- based analysis

7. Signal efficiency vs background rejection Can tune the PID cut for whatever signal efficiency, background rejection you want. What is the optimum?

8. Correlations 1: mean ph/plane vs track-like fraction Parameters are correlated – possible extra discrimination is thrown away when forming a product of 3 1D pdfs LHS:  CC RHS:  C

9. Correlations 2: mean ph/plane vs track planes

10. Correlations 3: track-like fraction vs track planes Double peak in  CC distribution

11. Combining 2D and 1D pdfs Use 2D pdf of ph per plane vs track fraction and 1D pdf of number of planes. This appears to provide slightly better NC/CC discrimination than a product of three 1D pdfs

12. 3D pdf CC NC CC

13. Performance of 3D pdf

14. Selection efficiencies for: 1D  1D  1D pdf 2D  1D pdf 3D pdf

15. Comparison of PID performances 3D pdf appears to provide by far the best discrimination However, the same events are used to define both the pdf and determine selection efficiencies 1D pdfs 2D  1D pdfs 3D pdf

16. Recent work on NC/CC separation Evaluate efficiencies with an independent sample of 15000 events – 3D efficiency falls apart. This is presumably due to statistical fluctuations in the 3D pdf, which is a 3D array with 50  50  50 bins 1D pdfs 2D  1D pdfs 3D pdf

17. Recent work on NC/CC separation 3D pdfs 50  50  50 bins, same sample 20  20  20 bins, same sample 20  20  20, independent sample Rebinned 3D pdf with fewer bins reduces this effect. Coarser granularity in pdf reduces low energy CC efficiency – large statistics MC required to make 3D pdfs work.

18. Conclusions Likelihood based analysis seems to provide slightly better discrimination than simple cuts using the same event variables This analysis only uses 3 variables to form the likelihood. I tried adding one or two more but did not achieve any gain. Important to account for correlations between variables Multi-dimensional pdfs require large MC statistics –Methods to smooth pdfs? Can fool yourself if you use the same sample to define pdf and evaluate NC/CC separation Need a method to optimise NC/CC selection. Current MC statistics modest – would like ~100K events to adequately define selection efficiencies