CGAN-Based Framework for Meson Mass and Width Prediction

TL;DR

This paper introduces a novel machine learning framework using CGANs to generate synthetic meson data, enabling improved predictions of meson mass and width, which are crucial for understanding strong interactions in QCD.

Contribution

The study applies CGANs to augment meson datasets and employs bagging to enhance prediction robustness, a novel approach in hadron spectroscopy.

Findings

CGAN models accurately describe meson properties

Synthetic data improves prediction accuracy

Method offers a new tool for hadron physics research

Abstract

Mesons play a crucial role in understanding the strong interaction in 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.