Study of exotic hadrons with machine learning

TL;DR

This paper employs deep neural networks to analyze the invariant mass spectrum of near-threshold exotic hadrons, extracting scattering parameters to better understand their nature, demonstrating advantages over traditional fitting methods especially with low-statistics data.

Contribution

The study introduces a neural network-based method for analyzing exotic hadron spectra, providing a more stable alternative to traditional fitting techniques for low-statistics experimental data.

Findings

Consistent scattering lengths and effective ranges with experimental fits.

Neural network analysis is more stable than traditional fitting for low-statistics data.

Method applicable to various one-channel near-threshold exotic states.

Abstract

We analyzed the invariant mass spectrum of near-threshold exotic states for one-channel candidates with a deep neural network. It can extract the scattering length and effective range, which would shed light on the nature of given states, from the experimental mass spectrum. As an application, the mass spectrum of the $X(3872)$ and the $T_{cc}^+$ are studied. The obtained scattering lengths, effective ranges, and most relevant thresholds are consistent with those from fitting to the experimental data. The advantage of the neural network is that it is more stable than the fitting, especially for low-statistic data. The network, which provides another way to analyze the experimental data, can also be applied to other one-channel near-threshold exotic candidates.