5/9/11 1

Update on TMVA

J. Bouchet

5/9/11 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

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Signal and background samples

• For the background, instead of hijing, I will try with real data

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Correlation matrix (signal)

QuickTime™ and a decompressor

are needed to see this picture.

A pdf version is at http://drupal.star.bnl.gov/STAR/system/files/correlation_matrix_signal.pdf

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Correlation matrix (background)

QuickTime™ and a decompressor

are needed to see this picture.

A pdf version is at http://drupal.star.bnl.gov/STAR/system/files/correlation_matrix_background.pdf

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Classifiers output distribution

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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

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Classifier Fisher > -.5 (embedding)

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Classifier Fisher > -.1 (embedding)

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Classifier Fisher > .1 (embedding)

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Classifier Fisher > .5 (embedding)

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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.

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Classifier BDT > -.3 (embedding)

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Classifier BDT > -.2 (embedding)

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Classifier BDT > -.1 (embedding)

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Classifier BDT > .1 (embedding)

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Classifier Fisher > -.1 (real data)

Note : this is only for data from 3 days, not all statistic

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Classifier Fisher > .1 (real)

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Classifier Fisher > .2 (real)

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Classifier Fisher > .4 (real)

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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)

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

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

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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)