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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/192647
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dc.contributor.authorNg, L.-
dc.contributor.authorBibrzycki, Ł-
dc.contributor.authorNys, J.-
dc.contributor.authorFernandez-Ramirez, César-
dc.contributor.authorPillon, Alessandro-
dc.contributor.authorMathieu, Vicent-
dc.contributor.authorRasmusson, A. J.-
dc.contributor.authorSzczepaniak, Adam Pawel-
dc.date.accessioned2023-01-26T12:14:11Z-
dc.date.available2023-01-26T12:14:11Z-
dc.date.issued2022-05-17-
dc.identifier.issn1550-7998-
dc.identifier.urihttps://hdl.handle.net/2445/192647-
dc.description.abstractWe perform the first amplitude analysis of experimental data using deep neural networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the Pc(4312) signal reported by the LHCb collaboration, and we find that its most likely interpretation is that of a virtual state. This method can be applied to other near-threshold resonance candidates.-
dc.format.extent6 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherAmerican Physical Society-
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1103/PhysRevD.105.L091501-
dc.relation.ispartofPhysical Review D, 2022, vol. 105, num. 9, p. L091501-1-L091501-6-
dc.relation.urihttps://doi.org/10.1103/PhysRevD.105.L091501-
dc.rights(c) American Physical Society, 2022-
dc.sourceArticles publicats en revistes (Física Quàntica i Astrofísica)-
dc.subject.classificationQuarks-
dc.subject.classificationPartícules (Matèria)-
dc.subject.otherQuarks-
dc.subject.otherParticles-
dc.titleDeep learning exotic hadrons-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.identifier.idgrec725660-
dc.date.updated2023-01-26T12:14:12Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
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