Artificial neural networks for neutron/? discrimination in the neutron detectors of NEDA

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dc.authoridBaulieu, Guillaume/0000-0002-9372-5523
dc.authoridNyberg, Johan/0000-0001-6996-7605
dc.authoridJaworski, Grzegorz/0000-0003-2241-0329
dc.authoridGonzalez Millan, Vicente/0000-0001-6014-2586
dc.contributor.authorFabian, X.
dc.contributor.authorBaulieu, G.
dc.contributor.authorDucroux, L.
dc.contributor.authorStezowski, O.
dc.contributor.authorBoujrad, A.
dc.contributor.authorClement, E.
dc.contributor.authorCoudert, S.
dc.date.accessioned2024-11-07T13:35:26Z
dc.date.available2024-11-07T13:35:26Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThree different Artificial Neural Network architectures have been applied to perform neutron/gamma discrimination in NEDA based on waveform and time-of-flight information. Using the coincident gamma-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms.
dc.description.sponsorshipNational Science Centre, Poland (NCN) [2017/25/B/ST2/01569]; STFC [ST/P003885/1, ST/L005727/1] Funding Source: UKRI
dc.description.sponsorshipOne of the author acknowledges support of the National Science Centre, Poland (NCN) (grant no. 2017/25/B/ST2/01569).
dc.identifier.doi10.1016/j.nima.2020.164750
dc.identifier.issn0168-9002
dc.identifier.issn1872-9576
dc.identifier.scopus2-s2.0-85092489481
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.nima.2020.164750
dc.identifier.urihttps://hdl.handle.net/11480/16503
dc.identifier.volume986
dc.identifier.wosWOS:000595155500019
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofNuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors and Associated Equipment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectgamma-ray spectroscopy
dc.subjectNeutron detector
dc.subjectn-gamma discrimination
dc.subjectPulse-shape discrimination
dc.subjectMachine learning
dc.subjectArtificial neural networks
dc.titleArtificial neural networks for neutron/? discrimination in the neutron detectors of NEDA
dc.typeArticle

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