Background rejection in NEXT using deep neural networks

Renner, J.
Farbin, A.
Muñoz Vidal, J.
Benlloch-Rodríguez, J.M.
Botas, A.
Ferrario, Paola
Gómez Cadenas, Juan José
Álvarez Puerta, Vicente
Azevedo, C.D.R.
Borges, F. I.G.
Cárcel García, Sara
Carrión, J. V.
Cebrián, S.
Cervera Villanueva, Anselmo
Conde, C.A.N.
Díaz Medina, José
Diesburg, M.
Esteve, R.
Fernandes, L.M.P.
Ferreira, A.L.
Freitas, E.D.C.
Goldschmidt, A.
González-Díaz, Diego
Gutiérrez, R.M.
Hauptman, J.
Henriques, C.A.O.
Hernando Morata, J.A.
Herrero, V.
Jones, B.
Labarga, L.
Laing, A.
Lebrun, P.
Liubarsky, Igor
López-March, N.
Lorca Galindo, David
Losada, M.
Martín-Albo Simón, Justo
Martínez-Lema, G.
Martínez, A.
Monrabal Capilla, Francesc
Monteiro, C.M.B.
Mora, F.J.
Moutinho, L.M.
Nebot Guinot, Miquel
Novella, P.
Nygren, D.
Palmeiro, B.
Para, A.
Pérez, J.
Querol, M.
Rodríguez, J.
Santos, F.P.
Dos Santos, J.M.F.
Serra, L.
Shuman, D.
Simón, A.
Sofka, C.
Sorel, Michel
Toledo, J.F.
Torrent Collell, J.
Tsamalaidze, Z.
Veloso, J.F.C.A.
White, J.T.
Webb, R.
Yahlali Haddou, Nadia
Yepes-Ramírez, H.
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 ​
This document is licensed under a Creative Commons:Attribution (by) Creative Commons by3.0