1. 5/9/111 Update on TMVA J. Bouchet

2. 5/9/112 What changed background and signal have increased statistic to recall, signal are (Kpi) pairs taken from single D 0, reconstructed through BFC chain and analyzed with MuKpi (unlike sign for daughters) ; background are pairs from hijing Au+Au @200 central event, reconstructed through BFC chain and analyzed with MuKpi (same sign for daughters) now TMVA takes almost all entries of the D0Tree : i had to remove some because it cannot compute (w/o change in the code ) with +35 variables : –the sign of daughters (assumption is done for sign(kaon) 0 –the signed decay lengths and errors of daughters from the secondary vertex The Fisher and BDT(boosted decisions trees) classifiers* have been used * : a classifier is a technique available with TMVA package used to discriminate signal from background

3. 5/9/113 Signal and background samples For the background, instead of hijing, I will try with real data

4. 5/9/114 Correlation matrix (signal) A pdf version is at http://drupal.star.bnl.gov/STAR/system/files/correlation_matrix_signal.pdf

5. 5/9/115 Correlation matrix (background) A pdf version is at http://drupal.star.bnl.gov/STAR/system/files/correlation_matrix_background.pdf

6. 5/9/116 Classifiers output distribution

7. 5/9/117 analysis After the training step, a file is created with the relation between the ‘user’ variable (p T D 0, slength,etc …) and the classifier output (see picture on the right) That means that for a given Kpi pair which has a unique set of variables in the D0Tree will correspond a unique classifier value.  Analysis consists to: run over data (embedding,real data, simulation) to fill another tree with the unique classifier value vary the classifier value and see how the inv. mass changes

8. 5/9/118 Classifier Fisher > -.5 (embedding)

9. 5/9/119 Classifier Fisher > -.1 (embedding)

10. 5/9/1110 Classifier Fisher >.1 (embedding)

11. 5/9/1111 Classifier Fisher >.5 (embedding)

12. 5/9/1112 comments A clear peak is seen when the classifier value is increased (note : this is embedding = flat ptD 0 …) We also see that for these high values, slength is strongly positive and p T D 0 ~3,4 GeV/c We also see that for the first value (>-.5) slength is wheter positive or negative but cosPointing is also strongly shifted towards 1 (before I had cosPointing shifted towards 1 only when cutting on slength>>0, so this may indicate another way of cutting on the variables than a simple cut on slength.

13. 5/9/1113 Classifier BDT > -.3 (embedding)

14. 5/9/1114 Classifier BDT > -.2 (embedding)

15. 5/9/1115 Classifier BDT > -.1 (embedding)

16. 5/9/1116 Classifier BDT >.1 (embedding)

17. 5/9/1117 Classifier Fisher > -.1 (real data) Note : this is only for data from 3 days, not all statistic

18. 5/9/1118 Classifier Fisher >.1 (real)

19. 5/9/1119 Classifier Fisher >.2 (real)

20. 5/9/1120 Classifier Fisher >.4 (real)

21. 5/9/1121 comments No inv. mass seen when increasing the classifier value (but for this sample only, I may use the full stat.) We see the same pattern as in embedding : –When classifier value increases, slength becomes strongly positive, p T D 0 around 3-4, cosPointing shifts towards 1 –Not what we want (high pTD 0 )

22. 5/9/1122 Classifier Fisher >.1 (sim : mixed D 0,D 0 bar+hijing)

23. 5/9/1123 Classifier Fisher >.1 (sim : mixed D 0,D 0 bar+hijing)

24. 5/9/1124 summary I have the macro to use the ouput of the classification. It works pretty well for embedding (but it uses flat p T D 0 ) We see slight differences btw the use of classifiers (Fisher vs. BDT) No significant results with real data (I may try with more stats) and simulation Next steps : –Look at the other methods –Try real data for background sample –Check the other D0Tree (than those shown in slide 8 to 24)