Background rejection in NEXT using deep neural networks

Renner, J.; Farbin, A.; Vidal, J. Muñoz; Benlloch-Rodríguez, J. M.; Botas, A.; Ferrario, P.; Gómez-Cadenas, J. J.; Álvarez, V.; Azevedo, C. D. R.; Borges, F. I. G.; Cárcel, S.; Carrión, J. V.; Cebrián, S.; Cervera, A.; Conde, C. A. N.; Díaz, J.; Diesburg, M.; Esteve, R.; Fernandes, L. M. P.; Ferreira, A. L.; Freitas, E. D. C.; Goldschmidt, A.; González-Díaz, D.; Gutiérrez, R. M.; Hauptman, J.; Henriques, C. A. O.; Morata, J. A.  Hernando; Herrero, V.; Jones, B.; Labarga, L.; Laing, A.; Lebrun, P.; Liubarsky, I.; López-March, N.; Lorca, D.; Losada, M.; Martín-Albo, J.; Martínez-Lema, G.; Martínez, A.; Monrabal, F.; Monteiro, C. M. B.; Mora, F. J.; Moutinho, L. M.; Nebot-Guinot, M.; Novella, P.; Nygren, D.; Palmeiro, B.; Para, A.; Pérez, J.; Querol, M.; Ripoll, L.; Rodríguez, J.; Santos, F. P.; Santos, J. M. F.  dos; Serra, L.; Shuman, D.; Simón, A.; Sofka, C.; Sorel, M.; Toledo, J. F.; Torrent, J.; Tsamalaidze, Z.; Veloso, J. F. C. A.; White, J.; Webb, R.; Yahlali, N.; Yepes-Ramírez, H.

doi:10.1088/1748-0221/12/01/T01004

Title: Background rejection in NEXT using deep neural networks

Journal Article · Mon Jan 16 00:00:00 EST 2017 · Journal of Instrumentation

DOI:https://doi.org/10.1088/1748-0221/12/01/T01004· OSTI ID:1346933

Renner, J.; Farbin, A.; Vidal, J. Muñoz; Benlloch-Rodríguez, J. M.; Botas, A.; Ferrario, P.; Gómez-Cadenas, J. J.; Álvarez, V.; Azevedo, C. D. R.; Borges, F. I. G.; Cárcel, S.; Carrión, J. V.; Cebrián, S.; Cervera, A.; Conde, C. A. N.; Díaz, J.; Diesburg, M.; Esteve, R.; Fernandes, L. M. P.; Ferreira, A. L. more »

Here, 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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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Organization:: NEXT Collaboration

Grant/Contract Number:: AC02-07CH11359

OSTI ID:: 1346933

Report Number(s):: FERMILAB-PUB-16-422-CD; arXiv:1609.06202; 1487439; TRN: US1700975

Journal Information:: Journal of Instrumentation, Vol. 12, Issue 01; ISSN 1748-0221

Publisher:: Institute of Physics (IOP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 39 works

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References (18)

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Cited By (4)

Machine learning at the energy and intensity frontiers of particle physics Radovic, Alexander; Williams, Mike; Rousseau, David Nature, Vol. 560, Issue 7716 https://doi.org/10.1038/s41586-018-0361-2	journal	August 2018
Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach Ai, P.; Wang, D.; Huang, G. Journal of Instrumentation, Vol. 14, Issue 03 https://doi.org/10.1088/1748-0221/14/03/p03002	journal	March 2019
High Pressure Gas Xenon TPCs for Double Beta Decay Searches Gomez-Cadenas, Juan J.; Monrabal Capilla, Francesc; Ferrario, Paola Frontiers in Physics, Vol. 7 https://doi.org/10.3389/fphy.2019.00051	journal	April 2019
High Pressure Gas Xenon TPCs for Double Beta Decay Searches Gomez-Cadenas, J. J.; Monrabal, F.; Ferrario, P. arXiv https://doi.org/10.48550/arxiv.1903.02435	preprint	January 2019

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