Deep Learning Exotic Hadrons

TL;DR

This paper introduces a model-independent deep learning approach to analyze experimental data, specifically examining the $P_c(4312)$ signal, and concludes it is most likely a virtual state, demonstrating the method's broader applicability.

Contribution

The paper presents the first use of deep neural networks for model-independent analysis of exotic hadron data, revealing the nature of the $P_c(4312)$ as a virtual state.

Findings

$P_c(4312)$ is most likely a virtual state

Deep neural networks can be used for model-independent analysis of hadron data

Method applicable to other near-threshold resonances

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.