Deep learning exotic hadrons
We 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 virtu...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/71951 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/71951 |
| Access Level: | acceso abierto |
| Palabra clave: | Partículas 2208 Nucleónica |
| Sumario: | We 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. |
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