Revealing the nature of hidden charm pentaquarks with machine learning

TL;DR

This paper uses neural networks within pionless effective field theory to analyze hidden charm pentaquarks, successfully distinguishing their quantum numbers and demonstrating the effectiveness of machine learning over traditional fitting methods.

Contribution

It introduces a neural network approach to identify quantum numbers of pentaquarks, outperforming traditional $ ext{chi}^2$ fitting in this context.

Findings

Neural networks can discriminate quantum numbers of pentaquarks.

Machine learning utilizes experimental data more effectively than traditional methods.

The approach provides new insights into the nature of exotic states.

Abstract

We study the nature of the hidden charm pentaquarks, i.e. the $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$, with a neural network approach in pionless effective field theory. In this framework, the normal $\chi^2$ fitting approach cannot distinguish the quantum numbers of the $P_c(4440)$ and $P_c(4457)$. In contrast to that, the neural network-based approach can discriminate them. In addition, we also illustrate the role of each experimental data bin of the invariant $J/\psi p$ mass distribution on the underlying physics in both neural network and fitting methods. Their similarities and differences demonstrate that neural network methods can use data information more effectively and directly. This study provides more insights about how the neural network-based approach predicts the nature of exotic states from the mass spectrum.