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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.17559 |
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| _version_ | 1866908462339850240 |
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| author | Li, Ruitian Luo, Xuan Sun, Hao Ortega, Pablo G. |
| author_facet | Li, Ruitian Luo, Xuan Sun, Hao Ortega, Pablo G. |
| contents | We develop a new method for solving two- and three-body bound state problems using unsupervised machine learning techniques. We use a deep neural network to calculate both simple and realistic potentials, obtaining the properties of the deuteron and triton bound states for the chiral effective field theory NN potential. Our results provide significant accuracy with no prior assumptions about the behaviour of the wave function. This neural network technique, which extends from two-body to three-body, may provide insight into potential solutions to the nuclear and hadronic many-body problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving two and three-body systems with deep neural networks Li, Ruitian Luo, Xuan Sun, Hao Ortega, Pablo G. High Energy Physics - Phenomenology Nuclear Theory We develop a new method for solving two- and three-body bound state problems using unsupervised machine learning techniques. We use a deep neural network to calculate both simple and realistic potentials, obtaining the properties of the deuteron and triton bound states for the chiral effective field theory NN potential. Our results provide significant accuracy with no prior assumptions about the behaviour of the wave function. This neural network technique, which extends from two-body to three-body, may provide insight into potential solutions to the nuclear and hadronic many-body problems. |
| title | Solving two and three-body systems with deep neural networks |
| topic | High Energy Physics - Phenomenology Nuclear Theory |
| url | https://arxiv.org/abs/2507.17559 |