Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
dc.contributor.author
dc.date.accessioned
2021-05-19T11:29:15Z
dc.date.available
2021-05-19T11:29:15Z
dc.date.issued
2021-01-21
dc.identifier.issn
1126-6708
dc.identifier.uri
dc.description.abstract
Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses
dc.description.sponsorship
This study used computing resources from Artemisa, co-funded by the European Union
through the 2014-2020 FEDER Operative Programme of the Comunitat Valenciana, project
DIFEDER/2018/048. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract
DE-AC02-06CH11357. The NEXT collaboration acknowledges support from the following
agencies and institutions: Xunta de Galicia (Centro singularde investigación de Galicia
accreditation 2019-2022), by European Union ERDF, and by the “María de Maeztu” Units
of Excellence program MDM-2016-0692 and the Spanish Research State Agency”; the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European
Union’s Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economía y
Competitividad and the Ministerio de Ciencia, Innovación y Universidades of Spain under
grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-2014-
0398 and CEX2018-000867-S; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS- NUC/2525/2014 and under projects UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE SC0019054
(University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015 18820. JMA acknowledges support from Fundación Bancaria “la Caixa” (ID 100010434), grant code
LCF/BQ/PI19/11690012. We also warmly acknowledge the Laboratori Nazionali del Gran
Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various
parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterráneo
de Canfranc for hosting and supporting the NEXT experiment
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1007/JHEP01(2021)189
dc.relation.ispartof
Journal of High Energy Physics, 2021, vol. 2021, art.núm.189
dc.relation.ispartofseries
Articles publicats (D-EMCI)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1029-8479