Building Hadron Potentials from Lattice QCD with Deep Neural Networks
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909335594991616 |
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| author | Wang, Lingxiao Doi, Takumi Hatsuda, Tetsuo Lyu, Yan |
| author_facet | Wang, Lingxiao Doi, Takumi Hatsuda, Tetsuo Lyu, Yan |
| contents | 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 Schrödinger-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 $Ω_{ccc}-Ω_{ccc}$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building Hadron Potentials from Lattice QCD with Deep Neural Networks Wang, Lingxiao Doi, Takumi Hatsuda, Tetsuo Lyu, Yan High Energy Physics - Lattice 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 Schrödinger-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 $Ω_{ccc}-Ω_{ccc}$ 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. |
| title | Building Hadron Potentials from Lattice QCD with Deep Neural Networks |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2410.03082 |