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Update on TMVA

Update on TMVA. J. Bouchet. What changed. background and signal have increased statistic

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Update on TMVA

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  1. Update on TMVA J. Bouchet

  2. What changed • background and signal have increased statistic • to recall, signal are (Kpi) pairs taken from single D0, 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 and sign(pion)>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. Signal and background samples • For the background, instead of hijing, I will try with real data

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

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

  6. Classifiers output distribution

  7. analysis • After the training step, a file is created with the relation between the ‘user’ variable (pTD0, 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. Classifier Fisher > -.5 (embedding)

  9. Classifier Fisher > -.1 (embedding)

  10. Classifier Fisher > .1 (embedding)

  11. Classifier Fisher > .5 (embedding)

  12. comments • A clear peak is seen when the classifier value is increased (note : this is embedding = flat ptD0…) • We also see that for these high values, slength is strongly positive and pTD0 ~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. Classifier BDT > -.3 (embedding)

  14. Classifier BDT > -.2 (embedding)

  15. Classifier BDT > -.1 (embedding)

  16. Classifier BDT > .1 (embedding)

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

  18. Classifier Fisher > .1 (real)

  19. Classifier Fisher > .2 (real)

  20. Classifier Fisher > .4 (real)

  21. 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 , pTD0 around 3-4, cosPointing shifts towards 1 • Not what we want (high pTD0)

  22. Classifier Fisher > .1 (sim : mixed D0,D0bar+hijing)

  23. Classifier Fisher > .1 (sim : mixed D0,D0bar+hijing)

  24. summary • I have the macro to use the ouput of the classification. • It works pretty well for embedding (but it uses flat pT D0) • 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)

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