1. Kalanand Mishra BaBar Coll. Meeting December, 2006 1/11 New Kaon Neural Net Selectors Kalanand Mishra University of Cincinnati  Overview  New selectors’ performance  Summary

2. Kalanand Mishra BaBar Coll. Meeting December, 2006 2/11 The input variables for neural net are: likelihoods from SVT, DCH, DRC (both global and track-based) and momentum and polar angle (  ) of the tracks. Separate neural net training for ‘Good Quality’ and ‘Poor Quality’* tracks: gives two family of selectors - “KNNGoodQual“ and “KNNNoQual”. Kaon neural net overview * Poor Quality tracks are defined as belonging to one of the following categories: - outside DIRC acceptance - passing through the cracks between DIRC bars - no DCH hits in layers > 35 - EMC energy < 0.15 GeV

3. Kalanand Mishra BaBar Coll. Meeting December, 2006 3/11 Performance of ‘KNNGoodQual’ selector Non-interesting Region Efficiency Purity The higher curve/ point represents better performance

4. Kalanand Mishra BaBar Coll. Meeting December, 2006 4/11 Performance in select momentum bins Low momentum: 0.3 < P < 0.5 GeV/c dE/dx - DRC transition region: 0.8 < P < 1.0 GeV/c Intermediate range: 1.9 < P < 2.1 GeV/c High momentum: 3.0 < P < 3.2 GeV/c

5. Kalanand Mishra BaBar Coll. Meeting December, 2006 5/11 ‘Good quality’ tracks: 0.3 < P < 0.5 GeV/c Efficiency Purity Non-interesting Region

6. Kalanand Mishra BaBar Coll. Meeting December, 2006 6/11 ‘Good quality’ tracks: 0.8 < P < 1.0 GeV/c Efficiency Purity Non-interesting Region

7. Kalanand Mishra BaBar Coll. Meeting December, 2006 7/11 ‘Good quality’ tracks: 1.9 < P < 2.1 GeV/c Efficiency Purity Non-interesting Region

8. Kalanand Mishra BaBar Coll. Meeting December, 2006 8/11 ‘Good quality’ tracks: 3.0 < P < 3.2 GeV/c Efficiency Purity Non-interesting Region

9. Kalanand Mishra BaBar Coll. Meeting December, 2006 9/11 Performance of ‘KNNNoQual’ selector Efficiency Purity

10. Kalanand Mishra BaBar Coll. Meeting December, 2006 10/11 New KNN selectors show significant improvement in performance over the old KNN selectors. Two different family of kaon neural net selectors available based on track quality. Each family has 4 levels of selection criteria : very loose, loose, tight, very tight. Analysts can tune in the performance of PID selection by making an optimal combination of ‘GoodQual’ and ‘NoQual’ selectors depending on the requirements of the analysis ( they have 4 x 4 = 16 choices, though some of the combinations do not make sense ! ).  New KNN selectors are competitive against likelihood selectors over a wide range of momentum and . Summary Continued …..

11. Kalanand Mishra BaBar Coll. Meeting December, 2006 11/11 The comparison of the overall performances of the neural net and likelihood based selectors is a bit complicated. For ‘GoodQual’ tracks, the LH selector seems to be performing better at tight level, but NN selector has an edge at loose level. For ‘NoQual’ tracks, NN selector performs better at every level. The new KNN selectors are now part of the latest version of ‘BetaPid’. I will finalize the fine-tuning of the neural net cuts and do bug fixing very soon (hopefully this week). And then the new KNN selectors will become part of the PID-table making machinery. Documentation to be completed by the end of January. Summary continued …..