Development of New SPR-based Kaon, Pion, Proton, and Electron Selectors

1. Development of New SPR-based Kaon, Pion, Proton, and Electron Selectors Kalanand Mishra University of Cincinnati

2. Two New Classes of Selectors • will replace the kaon neural net (KNN) selectors, currently being used in B-tagging • use Bagger Decision Tree algorithm (implemented in StatPatternRecognition package developed by Ilya Narsky) to separate kaon signal from pion background • will continue to provide kaon identification at 4 levels of strictness: Very Loose, Loose, Tight, Very Tight 1. BdtKaon Selectors: • are brand new selectors • separate kaon, pion, proton, electron from one another • use multi-class learning with bagger decision tree • will provide particle identification at 6 levels of strictness: Extra Loose, Very Loose, Loose, Tight, Very Tight, Extra Tight 2. KM Selectors:

3. Why New Selectors ? • For B-tagging, need new Kaon selector to replace the old KNN selectors. • - The KNN selectors haven’t been trained since circa 2001; there have been • many changes in the detector performance since then (e.g., Track Fix up, • Sasha Telnov’s corrections for dE/dx in DCH and SVT). • - Trained on Monte Carlo, but are used to evaluate performance in real data. • - Give degraded performance for high-momentum tracks. • - Need a general-purpose high-performance Kaon selector for Physics analysis. • For kaons, protons and pions, there is only one selector of choice for analysis: the LH selectors. There is room for improvement in performance. • For electron, the selector of choice at the moment is again the LH selector. • - It provides electron selection at only one level of strictness. • - Analysts have been asking for both looser and tighter level selections. • - Also some analyses and AWGs (notably Leptonic) will benefit enormously • from high-performance selectors for both low and high momentum tracks. • - The improvement in performance of this selector will be very important for • crucial BaBar analyses looking for New Physics, rare decays, CP violation ….

4. What is New in the New Selectors ? • Training on “real data”. • Include Sasha’s corrections for dE/dx in DCH and SVT. • Employ powerful statistical tools to separate signal and background, use bagging on weak classifier and multi-class training. • For each class of particle hypothesis: “kaon”, “pion”, “proton”, and “electron”, the other three classes are treated as background for classifier training. The only veto we need to apply is “muon veto” for ‘tight’ and ‘very tight’ selections. No additional vetoes. • Include many additional useful input variables, including P and  after flattening the two-dimensional P:  distribution. No need for separate trainings in P, bins.

5. StatPatternRecognition For details on the algorithms: arXiv:physics/0507143 (by Ilya Narsky) For illustration only Events • Decision Tree splits nodes recursively until a stopping criteria is satisfied. • Bagger decision tree divides the training data sample into a number of bootstrap replicas, and trains on each one of them separately. • The final classification is done by majority vote. Sgnl. Bkgd. Classifier Output

6. Performance of Bdt Kaon selectors Includes all momentum and  ranges and all tracks.

7. Bdt Kaon Performance in select mom. bins Caveat: I have changed the definition of the Y-axis variable. The higher curve/ point represents better performance dE/dx - DRC transition: 0.8 < P < 1 GeV/c High momentum: 3.0 < P < 3.2 GeV/c Intermediate range: 1.9 < P < 2.1 GeV/c

8. Bdt Kaon Performance by Track Quality Tracks passing through cracks between the DIRC bars Tracks in DIRC Caveat: The separation between the two categories is a bit fuzzy in my code ! Conclusion: Improvement in performance everywhere. Tracks passing through cracks between the DIRC bars: P > 1 GeV/c

9. Performance of KM selectors Number of data events used for each category = 1.22 x 105

10. Performance of Kaon selector LH Loose: Efficiency = 0.87 Pi Rej. = 0.96 LH Tight / VeryTight: Efficiency = 0.82 Pi Rej. = 0.98  LH V.Loose Caveat: These numbers are very crude approximations !

11. Performance of Pion selector LH Tight / Very Tight  Caveat: The numbers for LH selectors are very crude approximations !  LH Loose

12. Proton & Electron selectors Electron Proton Looks good…. But again need to see performance for the entire P,  spectrum.

13. Status  Deployment of New Selectors • A new selector class implemented in the latest version(s) of BetaPid. • Six levels of strictness: “extra loose”, “very loose”, “loose”, “tight”, “very tight”, “extra tight”. • Waiting for the new set of PID ntuples in 24 series. • After production on next set of PID ntuples, I plan to add additional discriminating variables: Likelihood Ratios. • Will do one final training and tune the “strictness” selection cuts for the final selectors. • The new selectors will be ready for deployment in Run 7 analysis series.