A neural network approach for two-body systems with spin and isospin degrees of freedom
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915942443778048 |
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| author | Wang, Chuanxin Naito, Tomoya Li, Jian Liang, Haozhao |
| author_facet | Wang, Chuanxin Naito, Tomoya Li, Jian Liang, Haozhao |
| contents | We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)], the present method enables consideration of the spin and isospin degrees of freedom by employing a non-fully connected deep neural network and the unsupervised machine learning technique. The validity of this method is verified by calculating the unique bound state of the deuteron. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_16819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A neural network approach for two-body systems with spin and isospin degrees of freedom Wang, Chuanxin Naito, Tomoya Li, Jian Liang, Haozhao Nuclear Theory Computational Physics Quantum Physics We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)], the present method enables consideration of the spin and isospin degrees of freedom by employing a non-fully connected deep neural network and the unsupervised machine learning technique. The validity of this method is verified by calculating the unique bound state of the deuteron. |
| title | A neural network approach for two-body systems with spin and isospin degrees of freedom |
| topic | Nuclear Theory Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2403.16819 |