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arXiv · 2410.03082

Building Hadron Potentials from Lattice QCD with Deep Neural Networks

Lingxiao Wang·Takumi Doi·Tetsuo Hatsuda·Yan Lyu
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Abstract

In this study, we develop a deep learning method to learn hadronic interactions unsupervisedly from the correlation functions calculated in lattice QCD simulations. We present our approach of using deep neural networks to model the inter-hadron potentials that are learned from Nambu-Bethe-Salpeter (NBS) wave functions. This enables the incorporation of most general forms of potentials into the Schr\"odinger-type equation for detailed analysis of hadronic interactions. Our results include validations with separable potentials, as well as the local and non-local potentials for the $\Omega_{ccc}-\Omega_{ccc}$ system. The neural networks accurately capture the essential features of these interactions, providing a reliable tool for predicting and analyzing hadron scattering properties, potentially bridging the experimental observables and lattice QCD data.

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Lingxiao Wang, Takumi Doi, Tetsuo Hatsuda, Yan Lyu. 2024-10-04. Building Hadron Potentials from Lattice QCD with Deep Neural Networks. https://arxiv.org/abs/2410.03082

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