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Title: Background rejection in NEXT using deep neural networks

Abstract

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.

Authors:
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Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
NEXT Collaboration
OSTI Identifier:
1346933
Report Number(s):
FERMILAB-PUB-16-422-CD; arXiv:1609.06202
Journal ID: ISSN 1748-0221; 1487439; TRN: US1700975
Grant/Contract Number:  
AC02-07CH11359
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 12; Journal Issue: 01; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; analysis and statistical methods; pattern recognition; cluster finding; calibration and fitting methods; double-beta decay detectors; time projection chambers

Citation Formats

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., and Yepes-Ramírez, H.. Background rejection in NEXT using deep neural networks. United States: N. p., 2017. Web. doi:10.1088/1748-0221/12/01/T01004.
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.. Background rejection in NEXT using deep neural networks. United States. doi:10.1088/1748-0221/12/01/T01004.
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., and Yepes-Ramírez, H.. Mon . "Background rejection in NEXT using deep neural networks". United States. doi:10.1088/1748-0221/12/01/T01004. https://www.osti.gov/servlets/purl/1346933.
@article{osti_1346933,
title = {Background rejection in NEXT using deep neural networks},
author = {Renner, J. and Farbin, A. and Vidal, J. Muñoz and Benlloch-Rodríguez, J. M. and Botas, A. and Ferrario, P. and Gómez-Cadenas, J. J. and Álvarez, V. and Azevedo, C. D. R. and Borges, F. I. G. and Cárcel, S. and Carrión, J. V. and Cebrián, S. and Cervera, A. and Conde, C. A. N. and Díaz, J. and Diesburg, M. and Esteve, R. and Fernandes, L. M. P. and Ferreira, A. L. and Freitas, E. D. C. and Goldschmidt, A. and González-Díaz, D. and Gutiérrez, R. M. and Hauptman, J. and Henriques, C. A. O. and Morata, J. A. Hernando and Herrero, V. and Jones, B. and Labarga, L. and Laing, A. and Lebrun, P. and Liubarsky, I. and López-March, N. and Lorca, D. and Losada, M. and Martín-Albo, J. and Martínez-Lema, G. and Martínez, A. and Monrabal, F. and Monteiro, C. M. B. and Mora, F. J. and Moutinho, L. M. and Nebot-Guinot, M. and Novella, P. and Nygren, D. and Palmeiro, B. and Para, A. and Pérez, J. and Querol, M. and Ripoll, L. and Rodríguez, J. and Santos, F. P. and Santos, J. M. F. dos and Serra, L. and Shuman, D. and Simón, A. and Sofka, C. and Sorel, M. and Toledo, J. F. and Torrent, J. and Tsamalaidze, Z. and Veloso, J. F. C. A. and White, J. and Webb, R. and Yahlali, N. and Yepes-Ramírez, H.},
abstractNote = {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.},
doi = {10.1088/1748-0221/12/01/T01004},
journal = {Journal of Instrumentation},
number = 01,
volume = 12,
place = {United States},
year = {Mon Jan 16 00:00:00 EST 2017},
month = {Mon Jan 16 00:00:00 EST 2017}
}

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