Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM

Detalles Bibliográficos
Autores: Kekic, M., NEXT collaboration, Labarga Echeverría, Luis Alfonso
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/704788
Acceso en línea:http://hdl.handle.net/10486/704788
https://dx.doi.org/10.1007/JHEP01(2021)189
Access Level:acceso abierto
Palabra clave:Beta Decay
Detector
Nuclear Matrix
Física
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spelling Demonstration of background rejection using deep convolutional neural networks in the NEXT experimentKekic, M.NEXT collaborationLabarga Echeverría, Luis AlfonsoBeta DecayDetectorNuclear MatrixFísicaArtículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMConvolutional 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 analysesSpringerDepartamento de Física TeóricaFacultad de Ciencias20212021-01-28research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/704788https://dx.doi.org/10.1007/JHEP01(2021)189reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 674896European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 690575European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 740055open accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7047882026-06-23T12:46:27Z
dc.title.none.fl_str_mv Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
title Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
spellingShingle Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
Kekic, M.
Beta Decay
Detector
Nuclear Matrix
Física
title_short Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
title_full Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
title_fullStr Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
title_full_unstemmed Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
title_sort Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
dc.creator.none.fl_str_mv Kekic, M.
NEXT collaboration
Labarga Echeverría, Luis Alfonso
author Kekic, M.
author_facet Kekic, M.
NEXT collaboration
Labarga Echeverría, Luis Alfonso
author_role author
author2 NEXT collaboration
Labarga Echeverría, Luis Alfonso
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Física Teórica
Facultad de Ciencias
dc.subject.none.fl_str_mv Beta Decay
Detector
Nuclear Matrix
Física
topic Beta Decay
Detector
Nuclear Matrix
Física
description Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-28
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/704788
https://dx.doi.org/10.1007/JHEP01(2021)189
url http://hdl.handle.net/10486/704788
https://dx.doi.org/10.1007/JHEP01(2021)189
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 674896
European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 690575
European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 740055
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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