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Background rejection in NEXT using deep neural networks

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Background rejection in NEXT using deep neural networks

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dc.contributor.author Renner, J. es_ES
dc.contributor.author Farbin, A. es_ES
dc.contributor.author Muñoz Vidal, J. es_ES
dc.contributor.author Benlloch-Rodríguez, J.M. es_ES
dc.contributor.author Botas, A. es_ES
dc.contributor.author Ferrario, P. es_ES
dc.contributor.author Gómez-Cadenas, J.J. es_ES
dc.contributor.author Álvarez, V. es_ES
dc.contributor.author Azevedo, C.D.R. es_ES
dc.contributor.author Borges, F.I.G. es_ES
dc.contributor.author Cárcel, S. es_ES
dc.contributor.author Carrión, J.V. es_ES
dc.contributor.author Esteve Bosch, Raul es_ES
dc.contributor.author Herrero Bosch, Vicente es_ES
dc.contributor.author Mora Mas, Francisco José es_ES
dc.contributor.author Toledo Alarcón, José Francisco es_ES
dc.date.accessioned 2017-05-18T12:37:33Z
dc.date.available 2017-05-18T12:37:33Z
dc.date.issued 2017-01
dc.identifier.issn 1748-0221
dc.identifier.uri http://hdl.handle.net/10251/81409
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the Ministerio de Economia y Competitividad of Spain and FEDER under grants CONSOLIDER-Ingenio 2010 CSD2008-0037 (CUP), FIS2014-53371-C04 and the Severo Ochoa Program SEV-2014-0398; GVA under grant PROMETEO/2016/120. Fermilab is operated by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the United States Department of Energy. JR acknowledges support from a Fulbright Junior Research Award. en_EN
dc.language Inglés es_ES
dc.publisher IOP Publishing es_ES
dc.relation MINECO/ CSD2008-0037 es_ES
dc.relation MINECO/FIS2014-53371-C04 es_ES
dc.relation MINECO/SEV-2014-0398 es_ES
dc.relation GV/PROMETEO/2016/120 es_ES
dc.relation DOE/DE-AC02-07CH11359 es_ES
dc.relation.ispartof Journal of Instrumentation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Analysis and statistical methods es_ES
dc.subject Double-beta decay detectors es_ES
dc.subject Time projection chambers es_ES
dc.subject cluster finding es_ES
dc.subject Pattern recognition es_ES
dc.subject calibration and fitting methods es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Background rejection in NEXT using deep neural networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1088/1748-0221/12/01/T01004
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/339787/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia es_ES
dc.description.bibliographicCitation Renner, J., Farbin, A., Vidal, J. M., Benlloch-Rodríguez, J. M., Botas, A., Ferrario, P., . . . Yepes-Ramírez, H. (2017). Background rejection in NEXT using deep neural networks. Journal of Instrumentation, 12(1)10.1088/1748-0221/12/01/T01004 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1088/1748-0221/12/01/T01004 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.relation.senia 329245 es_ES
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (MINECO)
dc.contributor.funder Generalitat Valenciana (GV)
dc.contributor.funder Department of Energy, EEUU (DOE)


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