2026-04-03T18:35:05Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/147512024-12-18T02:05:15Zcom_2072_452955com_2072_2054col_2072_453061

2024-06-14T08:52:10Z urn:hdl:10256/14751 Background rejection in NEXT using deep neural networks 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. Enginyeria -- Instruments Engineering instruments Anàlisi de conglomerats Cluster analysis 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 2024-06-14T08:52:10Z 2024-06-14T08:52:10Z 2017-01-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/10256/14751 info:eu-repo/semantics/altIdentifier/doi/10.1088/1748-0221/12/01/T01004 info:eu-repo/semantics/altIdentifier/issn/1748-0221 Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Institute of Physics (IOP) Journal of Instrumentation, 2017, vol. 12, p. T01004 Articles publicats (D-EMCI)