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arXiv · 2110.13742

Deep Learning Exotic Hadrons

JPAC Collaboration·L. Ng·L. Bibrzycki·J. Nys·C. Fernandez-Ramirez·A. Pilloni·V. Mathieu·A. J. Rasmusson·A. P. Szczepaniak
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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.

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JPAC Collaboration, L. Ng, L. Bibrzycki, J. Nys, C. Fernandez-Ramirez, A. Pilloni, V. Mathieu, A. J. Rasmusson, A. P. Szczepaniak. 2022-05-17. Deep Learning Exotic Hadrons. https://doi.org/10.1103/physrevd.105.l091501

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