Borodina, O.

Bozkurt, V.

Kim, E.

Maas, F.

Rappold, C.

Saito, T. R.

GSI Repository

Multivariate Analysis of the Phase 0 Experiment Data with TMVAC.Rappold∗1,2, O. Borodina1,3, V. Bozkurt1, E. Kim1, F. Maas1,4, and T.R. Saito1,2,41GSI, Darmstadt, Germany; 2JLU Giessen, Germany; 3Johannes Gutenberg-Universita¨t, Mainz, Germany; 4TheHelmholtz Institute Mainz, Mainz, GermanyThe first experiment of the HypHI collaboration aimedto demonstrate the feasibility of the hypernuclear spec-troscopy by means of heavy ion beam induced reactions.The phase 0 experiment was performed with a 6Li beamat 2AGeV impinged on a stable 12C target material. Themain goal of the experiment was to produce, reconstructand identify decay vertexes of Λ particle and 3ΛH,4ΛH and5ΛHe hypernuclei [1]. With the finalized data analysis ofPhase 0 experiment, the first results show that the experi-mental method is viable for the study of hypernuclei.The current analysis method involved series of rectan-gle cut over several geometrical observables in order to in-crease the signal-to-background ratio of the hypernuclearsignal over the combinatorial and physical background.The hypernuclear events are reconstructed by the invariantmass of the mother system which had decayed into 2- or 3-body daughter particles and fragments. The invariant massdistribution is the principal observable used to determinethe significance of the hypernuclear signal proportion.The TMVA package included inside the ROOT frame-work allows multivariate analysis of the data sets [2, 3].Those analysis methods have been employed to improvethe quality and purity of the hypernuclear data sets fromthe experimental data of the Phase 0 experiment. For eachspecies of interest, Λ, 3ΛH and4ΛH, a set of input observ-ables were selected for the discrimination of the signaland background component of the invariant mass distribu-tion. The signal data set used for the training of the differ-ent classifiers was characterized to be in the mass intervalm¯ ± 3 σm, where m¯ and σm were obtained from the sig-nal+background global fit of the invariant mass. The back-ground data set is then characterized by the side bands ofthe peak region ([m¯−6σm, m¯−3σm] and [m¯+3σm, m¯+6σm]). After the training of the classifiers of interest overa partial sample of the experimental data, those classifiersare tested to estimate their background rejection power andsignal efficiency. The top panel of Fig. 1 shows the re-sults of several multivariate classifiers over the 3ΛH data set.The used classifiers were based on the Projective Likeli-hood method (LikelihoodPCA, LikelihoodKDE), the Lin-ear Discriminant method (LD) and Artificial Neural Net-work methods (MLP, MLPBNN). In the case of the namedMLPBNN classifier, its output is shown in the bottom panelof Fig. 1. An output value close to 0 means that the event isconsidered as part of the background, while a value close to1 would mean a 3ΛH event. A threshold cut can be applied∗ c.rappold@gsi.dein order to select the most probable 3ΛH events. Thanks tothe classification of the events, the background contributioninside of the signal region m¯± 3 σm can be identified andthus rejected. By using the best classifier from the multi-variate analysis, one can obtain a data set of most probablehypernuclear events which can then be used for extractingdiverse physical observables with good statistics.Signal efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Background rejection0.20.30.40.50.60.70.80.91MVA Method:MLPBNNMLPLikelihoodPCALDLikelihoodKDECutsDBackground rejection versus Signal efficiencyMLPBNN response-0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4dx / (1/N) dN00.511.522.533.54SignalBackgroundU/O-flow (S,B): (0.0, 0.0)% / (0.0, 0.0)%TMVA response for classifier: MLPBNNFigure 1: Top panel : the Reciever Operating Characteris-tics“ROC” curve of the multivariate analysis of experimen-tal data for the 3ΛH hypernucleus. It represents backgroundrejection power as function of the signal efficiency obtainedfor each classifier used for different multivariate analysis.Bottom panel : Output of the Multilayer perceptron (ar-tificial neural network) classifier used to discriminate thesignal of 3ΛH hypernucleus (blue distribution) to the back-ground component (red distribution).References[1] Proposal of the HypHI Phase 0 experiment, the HypHI col-laboration.[2] A. Hoecker, et al. (2007) arXiv preprint physics/0703039.[3] P. Speckmayer, et al., Journal of Physics: Conference Series219 (2010) 032057.GSI SCIENTIFIC REPORT 2012 PHN-ENNA-EXP-31161

Multivariate Analysis of the Phase 0 Experiment Data with TMVA

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Multivariate Analysis of the Phase 0 Experiment Data with TMVA

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