Background rejection in NEXT using deep neural networks

Renner, J.; Lorca, D.; Gómez-Cadenas, J.J.; Martínez-Lema, G.; Martínez, A.; Toledo, J.F.; Álvarez, V.; Pérez, J.; Esteve, R.; Cebrián, S.; Monteiro, C.M.B.; Cervera, A.; Santos, F.P.; Freitas, E.D.C.; Conde, C.A.N.; Laing, A.; Ripoll, L.; Cárcel, S.; Novella, P.; Ferreira, A.L.; Lebrun, P.; Mora, F.J.; Monrabal, F.; Simón, A.; Goldschmidt, A.; López-March, N.; Vidal, J.M.; Carrión, J.V.; Serra, L.; Morata, J.A.H.; Ferrario, P.; Jones, B.; Hauptman, J.; Webb, R.; Martín-Albo, J.; Azevedo, C.D.R.; White, J.; Sorel, M.; Díaz, J.; Nebot-Guinot, M.; Liubarsky, I.; Benlloch-Rodríguez, J.M.; Yepes-Ramírez, H.; Farbin, A.; Rodríguez, J.; Querol, M.; Losada, M.; Henriques, C.A.O.; Yahlali, N.; Fernandes, L.M.P.; Labarga, L.; Diesburg, M.; González-Díaz, D.; Botas, A.; Moutinho, L.M.; Sofka, C.; Veloso, J.F.C.A.; Shuman, D.; Torrent, J.; Palmeiro, B.; Herrero, V.; Nygren, D.; Para, A.; Dos Santos, J.M.F.; Borges, F.I.G.; Gutiérrez, R.M.; Tsamalaidze, Z.

doi:10.1088/1748-0221/12/01/T01004

000060704 001__ 60704
000060704 005__ 20190709135454.0
000060704 0247_ $$2doi$$a10.1088/1748-0221/12/01/T01004
000060704 0248_ $$2sideral$$a98216
000060704 037__ $$aART-2017-98216
000060704 041__ $$aeng
000060704 100__ $$aRenner, J.
000060704 245__ $$aBackground rejection in NEXT using deep neural networks
000060704 260__ $$c2017
000060704 5060_ $$aAccess copy available to the general public$$fUnrestricted
000060704 5203_ $$aWe 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.
000060704 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-53371-C04$$9info:eu-repo/grantAgreement/ES/MINECO/Consolider-Ingenio-2010/CSD2008-00037$$9info:eu-repo/grantAgreement/EC/FP7/339787/EU/Towards the NEXT generation of bb0nu experimets/NEXT
000060704 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000060704 590__ $$a1.258$$b2017
000060704 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b44 / 61 = 0.721$$c2017$$dQ3$$eT3
000060704 592__ $$a0.642$$b2017
000060704 593__ $$aMathematical Physics$$c2017$$dQ2
000060704 593__ $$aInstrumentation$$c2017$$dQ2
000060704 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000060704 700__ $$aFarbin, A.
000060704 700__ $$aVidal, J.M.
000060704 700__ $$aBenlloch-Rodríguez, J.M.
000060704 700__ $$aBotas, A.
000060704 700__ $$aFerrario, P.
000060704 700__ $$aGómez-Cadenas, J.J.
000060704 700__ $$aÁlvarez, V.
000060704 700__ $$aAzevedo, C.D.R.
000060704 700__ $$aBorges, F.I.G.
000060704 700__ $$aCárcel, S.
000060704 700__ $$aCarrión, J.V.
000060704 700__ $$0(orcid)0000-0002-6948-5101$$aCebrián, S.$$uUniversidad de Zaragoza
000060704 700__ $$aCervera, A.
000060704 700__ $$aConde, C.A.N.
000060704 700__ $$aDíaz, J.
000060704 700__ $$aDiesburg, M.
000060704 700__ $$aEsteve, R.
000060704 700__ $$aFernandes, L.M.P.
000060704 700__ $$aFerreira, A.L.
000060704 700__ $$aFreitas, E.D.C.
000060704 700__ $$aGoldschmidt, A.
000060704 700__ $$aGonzález-Díaz, D.
000060704 700__ $$aGutiérrez, R.M.
000060704 700__ $$aHauptman, J.
000060704 700__ $$aHenriques, C.A.O.
000060704 700__ $$aMorata, J.A.H.
000060704 700__ $$aHerrero, V.
000060704 700__ $$aJones, B.
000060704 700__ $$aLabarga, L.
000060704 700__ $$aLaing, A.
000060704 700__ $$aLebrun, P.
000060704 700__ $$aLiubarsky, I.
000060704 700__ $$aLópez-March, N.
000060704 700__ $$aLorca, D.
000060704 700__ $$aLosada, M.
000060704 700__ $$aMartín-Albo, J.
000060704 700__ $$aMartínez-Lema, G.
000060704 700__ $$aMartínez, A.
000060704 700__ $$aMonrabal, F.
000060704 700__ $$aMonteiro, C.M.B.
000060704 700__ $$aMora, F.J.
000060704 700__ $$aMoutinho, L.M.
000060704 700__ $$aNebot-Guinot, M.
000060704 700__ $$aNovella, P.
000060704 700__ $$aNygren, D.
000060704 700__ $$aPalmeiro, B.
000060704 700__ $$aPara, A.
000060704 700__ $$aPérez, J.
000060704 700__ $$aQuerol, M.
000060704 700__ $$aRipoll, L.
000060704 700__ $$aRodríguez, J.
000060704 700__ $$aSantos, F.P.
000060704 700__ $$aDos Santos, J.M.F.
000060704 700__ $$aSerra, L.
000060704 700__ $$aShuman, D.
000060704 700__ $$aSimón, A.
000060704 700__ $$aSofka, C.
000060704 700__ $$aSorel, M.
000060704 700__ $$aToledo, J.F.
000060704 700__ $$aTorrent, J.
000060704 700__ $$aTsamalaidze, Z.
000060704 700__ $$aVeloso, J.F.C.A.
000060704 700__ $$aWhite, J.
000060704 700__ $$aWebb, R.
000060704 700__ $$aYahlali, N.
000060704 700__ $$aYepes-Ramírez, H.
000060704 7102_ $$12004$$2390$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Atóm.Molec.y Nucl.
000060704 773__ $$g12, 1 (2017), [21 pp]$$pJournal of Instrumentation$$tJournal of Instrumentation$$x1748-0221
000060704 8564_ $$s933477$$uhttps://zaguan.unizar.es/record/60704/files/texto_completo.pdf$$yVersión publicada
000060704 8564_ $$s64534$$uhttps://zaguan.unizar.es/record/60704/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000060704 909CO $$ooai:zaguan.unizar.es:60704$$particulos$$pdriver
000060704 951__ $$a2019-07-09-11:42:22
000060704 980__ $$aARTICLE

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