“School of Physics”
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| Paper IPM / P / 18277 |
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Fully-heavy tetraquark states, , have garnered significant attention both experimentally and theoretically, due to their unique properties and potential to provide new insights into Quantum Chromodynamics (QCD). In this study, we employ Conditional Generative Adversarial Networks (CGANs) to predict the masses and decay widths of fully-heavy tetraquarks. To deepen our understanding of heavy multiquark structures, we prepare datasets based on two distinct approaches and train the CGAN model using both. The CGAN framework allows us to capture the complex relationships between input features, such as quark content, quantum numbers, and Clebsch-Gordan coefficients, and output properties, including mass and decay width. Our predictions, based on the CGAN framework, are consistent with existing data. By combining fundamental knowledge of QCD with advanced machine learning techniques, this work represents a significant step forward in the theoretical understanding of fully-heavy tetraquark states. Our CGAN approach has the potential to become a strong contender for future studies in heavy tetraquark systems, complementing existing theoretical models to deliver more precise results. Additionally, our findings could assist in the search for fully-heavy tetraquark systems in future experiments.
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