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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)
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.
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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. https://doi.org/10.1088/1748-0221/12/01/T01004
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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. 2017. "Background rejection in NEXT using deep neural networks". United States. https://doi.org/10.1088/1748-0221/12/01/T01004. https://www.osti.gov/servlets/purl/1346933.
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@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},
url = {https://www.osti.gov/biblio/1346933}, journal = {Journal of Instrumentation},
issn = {1748-0221},
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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Publisher's Version of Record
https://doi.org/10.1088/1748-0221/12/01/T01004
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Works referenced in this record:

Neutrinoless double- β decay in SU(2)×U(1) theories
journal, June 1982

Discovery potential of xenon-based neutrinoless double beta decay experiments in light of small angular scale CMB observations
journal, March 2013

Double beta decay, Majorana neutrinos, and neutrino mass
journal, April 2008

Sensitivity of NEXT-100 to neutrinoless double beta decay
journal, May 2016

Near-intrinsic energy resolution for 30–662keV gamma rays in a high pressure xenon electroluminescent TPC
journal, April 2013

  • Álvarez, V.; Borges, F. I. G. M.; Cárcel, S.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 708
  • https://doi.org/10.1016/j.nima.2012.12.123

Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment
journal, April 2013

Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array
journal, September 2013

Characterisation of NEXT-DEMO using xenon K α X-rays
journal, October 2014

First proof of topological signature in the high pressure xenon gas TPC with electroluminescence amplification for the NEXT experiment
journal, January 2016

Search for ββ decay in 136Xe: new results from the Gotthard experiment
journal, August 1998

An homeopathic cure to pure Xenon large diffusion
journal, February 2016

Geant4—a simulation toolkit
journal, July 2003

  • Agostinelli, S.; Allison, J.; Amako, K.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3
  • https://doi.org/10.1016/S0168-9002(03)01368-8

Event generator DECAY4 for simulating double-beta processes and decays of radioactive nuclei
journal, July 2000

A convolutional neural network neutrino event classifier
journal, September 2016

Searching for exotic particles in high-energy physics with deep learning
journal, July 2014

Jet-images — deep learning edition
journal, July 2016

Review of Particle Physics
journal, October 2016

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    Works referencing / citing this record:

    Machine learning at the energy and intensity frontiers of particle physics
    journal, August 2018

    High Pressure Gas Xenon TPCs for Double Beta Decay Searches
    journal, April 2019

    High Pressure Gas Xenon TPCs for Double Beta Decay Searches
    preprint, January 2019

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