Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless...

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Detalles Bibliográficos
Autores: Kekic, M, Adams, C, Woodruff, K, Renner, J, Church, E, Del Tutto, M, Morata, JAH, Gomez-Cadenas, JJ, Alvarez, V, Arazi, L, Arnquist, IJ, Azevedo, CDR, Bailey, K, Ballester, F, Benlloch-Rodriguez, JM, Borges, FIGM, Byrnes, N, Carcel, S, Carrion, JV, Cebrian, S, Conde, CAN, Contreras, T, Diaz, G, Diaz, J, Diesburg, M, Escada, J, Esteve, R, Felkai, R, Fernandes, AFM, Fernandes, LMP, Ferrario, P, Ferreira, AL, Freitas, EDC, Generowicz, J, Ghosh, S, Goldschmidt, A, Gonzalez-Diaz, D, Guenette, R, Gutierrez, RM, Haefner, J, Hafidi, K, Hauptman, J, Henriques, CAO, Herrero, P, Herrero, V, Ifergan, Y, Jones, BJP, Labarga, L, Laing, A, Lebrun, P, Lopez-March, N, Losada, M, Mano, RDP, Martin-Albo, J, Martinez, A, Martinez-Lema, G, Martinez-Vara, M, McDonald, AD, Meziani, ZE, Monrabal, F, Monteiro, CMB, Mora, FJ, Vidal, JM, Novella, P, Nygren, DR, Palmeiro, B, Para, A, Perez, J, Querol, M, Redwine, AB, Ripoll, L, Garcia, YR, Rodriguez, J, Rogers, L, Romeo, B, Romo-Luque, C, Santos, FP, dos Santos, JMF, Simon, A, Sofka, C, Sorel, M, Stiegler, T, Toledo, JF, Torrent, J, Uson, A, Veloso, JFCA, Webb, R, Weiss-Babai, R, White, JT, Yahlali, N
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Universidad de Zaragoza
Repositorio:Zaguán. Repositorio Digital de la Universidad de Zaragoza
OAI Identifier:oai:zaguan.unizar.es:110870
Acceso en línea:http://zaguan.unizar.es/record/110870
Access Level:acceso abierto
Descripción
Sumario:Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.

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