Deep learning exotic hadrons

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dc.contributor.authorNg, L.
dc.contributor.authorBibrzycki, Ł.
dc.contributor.authorNys, J.
dc.contributor.authorFernández Ramírez, César
dc.contributor.authorPilloni, A.
dc.contributor.authorMathieu, Vincent
dc.contributor.authorRasmusson, A. J.
dc.contributor.authorSzczepaniak, A. P.
dc.date.accessioned2023-06-22T10:58:03Z
dc.date.available2023-06-22T10:58:03Z
dc.date.issued2022-05-17
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 P c ( 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.description.departmentDepto. de Física Teórica
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipUnión Europea. Horizonte 2020
dc.description.sponsorshipMinisterio de Ciencia e Innovación (MICINN)
dc.description.sponsorshipT
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/74477
dc.identifier.doi10.1103/PhysRevD.105.L091501
dc.identifier.issn2470-0010
dc.identifier.officialurlhttps://doi.org/10.1103/PhysRevD.105.L091501
dc.identifier.urihttps://hdl.handle.net/20.500.14352/71951
dc.issue.number9
dc.journal.titlePhysical Review D
dc.language.isoeng
dc.publisherAPS Physics
dc.relation.projectIDFELLINI (754496)
dc.relation.projectIDPID2019–106080 GB-C21; PID2020–118758GB-I00.
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/es/
dc.subject.ucmPartículas
dc.subject.unesco2208 Nucleónica
dc.titleDeep learning exotic hadrons
dc.typejournal article
dc.volume.number105
dspace.entity.typePublication
relation.isAuthorOfPublication51733d00-4a0c-402c-8870-a26d1b00ea00
relation.isAuthorOfPublication17de49b7-46a8-4bce-b8df-ee29abc569d8
relation.isAuthorOfPublication.latestForDiscovery51733d00-4a0c-402c-8870-a26d1b00ea00

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