📝 SummarXiv

Abstract

Mesons play a crucial role in understanding the strong interactionin the framework of quantum chromodynamics (QCD). However, the mass and decay width of several ordinary and exotic mesons remain experimentally undetermined. In this work, we propose a novel application of advanced machine learning techniques to deal with this challenge. Due to the limited available meson datasets, traditional data-driven methods are norm To overcome this, we employ a Conditional Generative Adversarial Network (CGAN) to generate synthetic meson data based on known physical parameters. This not only augments the dataset but also retain the underlying physics of the original mesons data. With the extended dataset, we train multiple copies of CGAN and apply a bagging technique to predict uncertainties, improving the robustness and reliability of the predictions. As our findings indicate, the CGAN models are capable of well describing meson properties and their structure relations, offering a potent novel instrument for hadron spectroscopy. This calculation opens a promising future for data-driven hadron physics studies.