e/pi separation in CalDet and nue identification in MDC

1. e/pi separation in CalDet and nue identification in MDC T.J. Yang Stanford University

2. e/pi separation Thanks to great help from Patricia Vahle and helpful discussion with Adam Para

3. What do an electron and a pion look like in CalDet? P=1GeV/c e pi

4. What do an electron and a pion look like in CalDet? P=2GeV/c e pi

5. What do an electron and a pion look like in CalDet? P=3GeV/c e pi

6. Information from CalDet data • EM shower is more compact than hadronic shower. And most of the EM showers have the same pattern regardless of energy: 4-5 planes long and 1-2 strip(s) wide. • EM shower develops much faster than hadronic shower. • Pions usually achieve the maximum energy lost later than electrons.

7. Fractions of energy deposited in 1,2,3,4,5,6 consecutive plane(s) with the highest ph

8. Fractions of energy deposited in 2,4,6,8,10,12 counters with the highest ph

9. Fraction of energy in a narrow road

10. Ratio of energy deposited in the first (second) plane to the second (third) plane

11. Shower_max Position (with respect to the first plane) of the plane with the highest energy lost in the event.

12. Summary of discriminating variables (reference:NuMI-L-284) Longitudinal features: • Track length • Fractions of energy deposited in 1,2,3,4,5,6 consecutive plane(s) with the highest p.h. • Ratio of energy deposited in plane 0 to plane 1 • Position of the maximum energy lost Lateral features: • Fraction of energy deposited in a narrow road Others: • Fractions of energy deposited in 2,4,6,8,10,12 strips with the highest p.h.

13. Run numbers used for CalDet e/pi separation analysis

14. Results

15. Results (continued) Hadron rejection ~1e-4 – 1e-3 for e~10-20%

16. nue identification

17. Files used for nue analysis • beam files: f24100001_0000.sntp.R1.9.root -f24100020_0000.sntp.R1.9.root (all) • nue files: f24110001_0000.sntp.R1.9.root – f24110009_0000.sntp.R1.9.root (all) • nutau files: f24130001_0000.sntp.R1.9.root – f24130020_0000.sntp.R1.9.root (half)

18. Statistics and parameters • numu->nue: ~26,000 NC: ~33,000 CC: ~66,000 • |Ue3|2 = 0.01 • m2 = 2.5e-3eV2 • assume a 2.5-yr run • pot/yr = 3.7e20

19. Strategy • Making cuts on the variables from SR: total p.e. , track range, shower range and total number of strips • Calculating the same variables as I used in the CalDet e/pi separation analysis • Using neural network and boosted decision trees to get the optimal results

20. Total pe (ph.pe)

21. Track range (trk.ds)

22. Shower range (shw.plane.n)

23. Total number of strips (nstrip)

24. Cuts on SR variables • 200<ph.pe<700 • trk.ds<0.9 • 5<shw.plane.n<14 • 14<nstrip<56

25. Calculating variables • Fractions of energy deposited in 1,2,3,4,5,6 consecutive plane(s) with the highest ph • Fractions of energy which are deposited in 2,4,6,8,10,12 counters with the highest ph • Fraction of energy in a narrow road Problems: • Distributions don’t look quite different between signal and backgrounds • After the preliminary cuts, the distributions look almost the same

26. Fraction of energy deposited in 3 consecutive planes

27. Fraction of energy deposited in 3 counters with the highest ph

28. Fraction of energy in a narrow road

29. The use of neural network • Input variables: fract_1_cons, fract_2_cons, fract_3_cons, fract_4_cons, fract_5_cons, fract_6_cons, fract_1_count, fract_2_count, fract_3_count, fract_4_count, fract_6_count, fract_8_count, fract_ct, fract_ct_2, rms1u, rms1v • Structure: 17:14:1 • Tried two different packages: Jetnet and TMultiLayerPerceptron(MLP) in ROOT

30. Jetnet (training)

31. Jetnet (testing)

32. MLP (training)

33. MLP (testing)

34. Boosted Decision Trees Jake Klamka University of Toronto

35. Boosted Decision Trees • In the past several years there has been a “revolution in the field of machine learning inspired by the extension of decision trees by boosting.” (J. Friedman -- PHYSTAT2003) • BOOSTING: A procedure that combines the outputs of many “weak” classifiers to produce a powerful “committee”. • Multiple decision trees are created using weighted versions of the training dataset. Final event classification is based on a linear combination of individual decision trees.

36. Rough Outline of a Boosting Algorithm: • Weight all events in the data sample equally. • ** LOOP **: • Train new decision tree with current event weights. • Re-weight events, giving higher weight to events that were misclassified in (a). • Take the linear combination of all decision trees with most accurate decision trees given more weight. AdaBoost.M1 (Freund and Schapire 1996)

37. Software: See5 (C5) • Commercial decision tree software created by Ross Quinlan. • New version allows for boosting. Previous version known as C4.5. • Pros: 10 days free trial, easy to install and setup, very fast classification. • Cons: Exact boosting algorithm used by See5 is unknown (proprietary), not easily customizable.

38. Results with decision trees(no boosting) • no boosting, cost 1:1 • no boosting, cost 1:2

39. Results with boosted decision trees • boosting: 3 trials, cost 1:1 • boosting: 3 trials, cost 1:2

40. BOOSTING in NEUTRINO PHYSICS: • The MiniBooNE collaboration is using boosted decision trees and has announced 20% to 80% improvements over their best results with neural networks. Their analysis is described in a recent preprint and their code is available online. (physics/0408124) FUTURE PROSPECTS: • Customized boosted decision tree code (use MiniBooNE code as prototype?) • Try different boosting algorithms. • Boosting algorithm can be applied to any classification technique, not just decision trees…“Boosted Neural Networks” may offer even better results. More information and links: http://home.fnal.gov/~jklamka/

41. Summary

42. Conclusion • For the CalDet e/pi separation study, we got the hadron rejection ~1e-4 – 1e-3 for e~10-20% • For the nue identification in MDC, we got FOM ~0.94 • We will try to understand several issues and modify the variables to improve the FOM.