Identifying Hadronic Molecular States with a Neural Network

TL;DR

This paper introduces a neural network approach to identify hadronic molecular states from line-shapes, successfully classifying several exotic states and providing insights into their molecular nature.

Contribution

The study develops and applies a neural network method to determine the molecular nature of exotic hadronic states based on their line-shapes, demonstrating its effectiveness.

Findings

Z_c(3900) is likely a D* D molecular state

X(3872) is not a molecular state

X(4260) cannot be a χ_c0 ω molecular state

Abstract

Neural networks are trained to judge whether or not an exotic state is a hadronic molecule of a given channel according its line-shapes. This method performs well in both trainings and validation tests. As applications, it is applied to study $X(3872)$, $X(4260)$ and $Z_c(3900)$. The results show that $Z_c(3900)$ should be regarded as a $\bar{D}^* D$ molecular state but $X(3872)$ not. As for $X(4260)$, it can not be a molecular state of $\chi_{c0}\omega$. Some discussions on $X_1(2900)$ are also provided.