Machine Learning for Extrapolating No-Core Shell Model Results to Infinite Basis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910907019296768 |
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| author | Sharypov, R. E. Mazur, A. I. Shirokov, A. M. |
| author_facet | Sharypov, R. E. Mazur, A. I. Shirokov, A. M. |
| contents | We utilize the machine learning to extrapolate to the infinite model space the no-core shell model (NCSM) results for the energies and rms radii of the 6He ground state and 6Li lowest states. The extrapolated energies and rms radii converge as the NCSM results from larger model spaces are included in the training dataset for ensemble of artificial neural networks thus enabling an accurate predictions for these observables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06594 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Extrapolating No-Core Shell Model Results to Infinite Basis Sharypov, R. E. Mazur, A. I. Shirokov, A. M. Nuclear Theory We utilize the machine learning to extrapolate to the infinite model space the no-core shell model (NCSM) results for the energies and rms radii of the 6He ground state and 6Li lowest states. The extrapolated energies and rms radii converge as the NCSM results from larger model spaces are included in the training dataset for ensemble of artificial neural networks thus enabling an accurate predictions for these observables. |
| title | Machine Learning for Extrapolating No-Core Shell Model Results to Infinite Basis |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2504.06594 |