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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Detalles 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 recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/14751
Acceso en línea:http://hdl.handle.net/10256/14751
Access Level:acceso abierto
Palabra clave:Enginyeria -- Instruments
Engineering instruments
Anàlisi de conglomerats
Cluster analysis
Descripción
Sumario: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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