Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
Entity
UAM. Departamento de Física TeóricaPublisher
SpringerDate
2021-01-28Citation
10.1007/JHEP01(2021)189
Journal of High Energy Physics 1 (2021): 189
ISSN
1126-6708 (print); 1029-8479 (online)DOI
10.1007/JHEP01(2021)189Project
info:eu-repo/grantAgreement/EC/H2020/674896/EU//ELUSIVES; info:eu-repo/grantAgreement/EC/H2020/690575/EU//InvisiblesPlus; info:eu-repo/grantAgreement/EC/H2020/740055/EU//MELODIC; Gobierno de España. FIS2014-53371-CO4; Gobierno de España. SEV-2014-0398; Gobierno de España. CEX2018-000867-S-20-1Editor's Version
https://doi.org/10.1007/JHEP01(2021)189Subjects
Beta Decay; Detector; Nuclear Matrix; FísicaNote
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 UAMRights
© 2021, The Author(s)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
Files in this item
Google Scholar:Kekic, M.
-
NEXT collaboration
-
Labarga Echeverría, Luis Alfonso
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.