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Article
Report number arXiv:1609.06202 ; FERMILAB-PUB-16-422-CD
Title Background rejection in NEXT using deep neural networks
Author(s)

Renner, J. (Valencia U., IFIC ; Valencia U.) ; Farbin, A. (Texas U., Arlington) ; Vidal, J. Muñoz (Valencia U., IFIC ; Valencia U.) ; Benlloch-Rodríguez, J.M. (Valencia U., IFIC ; Valencia U.) ; Botas, A. (Valencia U., IFIC ; Valencia U.) ; Ferrario, P. (Valencia U., IFIC) ; Gómez-Cadenas, J.J. (Valencia U., IFIC ; Valencia U.) ; Álvarez, V. (Valencia U., IFIC ; Valencia U.) ; Azevedo, C.D.R. (U. Aveiro (main)) ; Borges, F.I.G. (Coimbra U.) ; Cárcel, S. (Valencia U., IFIC ; Valencia U.) ; Carrión, J.V. (Valencia U., IFIC ; Valencia U.) ; Cebrián, S. (U. Zaragoza, LFNAE) ; Cervera, A. (Valencia U., IFIC ; Valencia U.) ; Conde, C.A.N. (Coimbra U.) ; Díaz, J. (Valencia U., IFIC ; Valencia U.) ; Diesburg, M. (Fermilab) ; Esteve, R. (Valencia, Polytechnic U.) ; Fernandes, L.M.P. (Coimbra U.) ; Ferreira, A.L. (U. Aveiro (main)) ; Freitas, E.D.C. (Coimbra U.) ; Goldschmidt, A. (LBL, Berkeley) ; González-Díaz, D. (CERN) ; Gutiérrez, R.M. (Antonio Narino U.) ; Hauptman, J. (Iowa State U.) ; Henriques, C.A.O. (Coimbra U.) ; Hernando Morata, J. A. (Santiago de Compostela U., IGFAE) ; Herrero, V. (Valencia, Polytechnic U.) ; Jones, B. (Texas U., Arlington (main)) ; Labarga, L. (Madrid, Autonoma U.) ; Laing, A. (Valencia U., IFIC ; Valencia U.) ; Lebrun, P. (Fermilab) ; Liubarsky, I. (Valencia U., IFIC ; Valencia U.) ; López-March, N. (Valencia U., IFIC ; Valencia U.) ; Lorca, D. (Valencia U., IFIC ; Valencia U.) ; Losada, M. (Antonio Narino U.) ; Martín-Albo, J. (Oxford U.) ; Martínez-Lema, G. (Santiago de Compostela U., IGFAE) ; Martínez, A. (Valencia U., IFIC ; Valencia U.) ; Monrabal, F. (Valencia U., IFIC ; Valencia U.) ; Monteiro, C.M.B. (Coimbra U.) ; Mora, F.J. (Valencia, Polytechnic U.) ; Moutinho, L.M. (U. Aveiro (main)) ; Nebot-Guinot, M. (Valencia U. ; Valencia U., IFIC) ; Novella, P. (Valencia U., IFIC ; Valencia U.) ; Nygren, D. (Texas U., Arlington (main)) ; Palmeiro, B. (Valencia U., IFIC) ; Para, A. (Fermilab) ; Pérez, J. (Madrid, Autonoma U.) ; Querol, M. (Valencia U., IFIC ; Valencia U.) ; Ripoll, L. (Girona U.) ; Rodríguez, J. (Valencia U., IFIC ; Valencia U.) ; Santos, F.P. (Coimbra U.) ; dos Santos, J.M.F. (Coimbra U.) ; Serra, L. (Valencia U. ; Valencia U., IFIC) ; Shuman, D. (LBL, Berkeley) ; Simón, A. (Valencia U., IFIC ; Valencia U.) ; Sofka, C. (Texas A-M) ; Sorel, M. (Valencia U., IFIC ; Valencia U.) ; Toledo, J.F. (Valencia, Polytechnic U.) ; Torrent, J. (Girona U.) ; Tsamalaidze, Z. (Dubna, JINR) ; Veloso, J.F.C.A. (Aveiro U.) ; White, J. (Texas A-M) ; Webb, R. (Texas A-M) ; Yahlali, N. (Valencia U., IFIC ; Valencia U.) ; Yepes-Ramírez, H. (Antonio Narino U.)

Publication 2017-01-16
Imprint 20 Sep 2016
Number of pages 21
Note 21 pages, 9 figures; formatting changes
In: JINST 12 (2017) T01004
DOI 10.1088/1748-0221/12/01/T01004
Subject category hep-ex ; Particle Physics - Experiment ; physics.ins-det ; Detectors and Experimental Techniques
Accelerator/Facility, Experiment NEXT
Abstract 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.
Copyright/License arXiv nonexclusive-distrib. 1.0
publication: © 2017-2024 The Author(s) (License: CC-BY-3.0)



Corresponding record in: Inspire


 Registre creat el 2016-09-22, darrera modificació el 2022-08-10


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