Background rejection in NEXT using deep neural networks

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...

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Detalhes bibliográficos
Autores: Renner, Joshua, 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, Filipa I.G.M., Cárcel García, Sara, Carrión, J.V., Cebrián, Susana, Cervera Villanueva, Anselmo, Conde, Carlos A.N., Díaz Medina, José, Diesburg, M., Esteve, Raúl, Fernandes, L.M.P., Ferreira, Antonio Luis, Freitas, Elisabete D.C., Goldschmidt, Azriel, González-Díaz, Diego, Gutiérrez, Rafael María, Hauptman, John M., Henriques, C.A.O., Hernando Morata, J.A., Herrero, Vicente, Jones, B., Labarga, Luis A., Laing, Andrew, Lebrun, P., Liubarsky, Igor, López-March, N., Lorca Galindo, David, Losada, Marta, Martín-Albo Simón, Justo, Martínez Lema, Gonzalo, Martínez Pérez, Alberto, Monrabal Capilla, Francesc, Monteiro, Cristina M.B., Mora, Francisco José, Moutinho, L.M., Nebot Guinot, Miquel, Novella, P., Nygren, David R., Palmeiro, B., Para, A., Pérez, Javier Martin, Querol, M., Ripoll Masferrer, Lluís, Rodríguez Samaniego, Javier, Santos, Filomena P., dos Santos, Joaquim M.F., Serra Díaz-Cano, Luis, Shuman, Derek B., Simón Estévez, Ander, Sofka, C., Sorel, Michel, Toledo, J.F., Torrent Collell, Jordi, Tsamalaidze, Zviadi, Veloso, João F.C.A., White, James T., Webb, R.C., Yahlali Haddou, Nadia, Yepes-Ramírez, H.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2017
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/14751
Acesso em linha:http://hdl.handle.net/10256/14751
Access Level:Acceso aberto
Palavra-chave:Enginyeria -- Instruments
Engineering instruments
Anàlisi de conglomerats
Cluster analysis
Descrição
Resumo: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

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