We perform the first model independent 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.

Deep Learning Exotic Hadrons

A. Pilloni;
2022-01-01

Abstract

We perform the first model independent 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.
2022
Si
105
9
091501
091506
6
Internazionale
Esperti anonimi
High Energy Physics - Phenomenology; High Energy Physics - Phenomenology; High Energy Physics - Experiment; Nuclear Theory
info:eu-repo/semantics/article
Ng, L.; Bibrzycki, L.; Nys, J.; Fernandez-Ramirez, C.; Pilloni, A.; Mathieu, V.; Rasmusson, A. J.; Szczepaniak, A. P.
14.a Contributo in Rivista::14.a.1 Articolo su rivista
8
262
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3232151
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