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Background rejection in NEXT using deep neural networks

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arxiv 1609.06202 v3 pith:6OYHHLPG submitted 2016-09-20 physics.ins-det hep-ex

NEXT Collaboration: J. Renner , A. Farbin , J. Muñoz Vidal , J.M. Benlloch-Rodríguez , A. Botas , P. Ferrario , J.J. Gómez-Cadenas , V. Álvarez
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C.D.R. Azevedo F.I.G. Borges S. Cárcel J.V. Carrión S. Cebrián A. Cervera C.A.N. Conde J. Díaz M. Diesburg R. Esteve L.M.P. Fernandes A.L. Ferreira E.D.C. Freitas A. Goldschmidt D. González-Díaz R.M. Gutiérrez J. Hauptman C.A.O. Henriques J.A. Hernando Morata V. Herrero B. Jones L. Labarga A. Laing P. Lebrun I. Liubarsky N. López-March D. Lorca M. Losada J. Martín-Albo G. Martínez-Lema A. Martínez F. Monrabal C.M.B. Monteiro F.J. Mora L.M. Moutinho M. Nebot-Guinot P. Novella D. Nygren A. Para J. Pérez M. Querol L. Ripoll J. Rodríguez F.P. Santos J.M.F. dos Santos L. Serra D. Shuman A. Simón C. Sofka M. Sorel J.F. Toledo J. Torrent Z. Tsamalaidze J.F.C.A. Veloso J. White R. Webb N. Yahlali H. Yepes-Ramírez
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classification physics.ins-dethep-ex
keywords backgroundeventsnetworksdeepfactorfurtherneuralpotential
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We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

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