Data-driven trap theory for nuclear scattering
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908538263044096 |
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| author | Zhang, Hantao Bai, Dong Zhang, Xilin Ren, Zhongzhou |
| author_facet | Zhang, Hantao Bai, Dong Zhang, Xilin Ren, Zhongzhou |
| contents | We present a novel data-driven trap theory (abbreviated as DDTT) for nuclear scattering, which aims to overcome the limitations of the traditional trap method in dealing with narrow potential wells, while also providing a more efficient framework for handling long-range Coulomb interactions. As proof-of-concept examples, we employ this unified theory to analyze the elastic scattering of nucleon-nucleon and nucleon-α systems. DDTT can successfully produce results consistent with those from traditional approaches, highlighting its significance for ab initio light nuclei scattering studies and potential for applications in the heavier mass region. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven trap theory for nuclear scattering Zhang, Hantao Bai, Dong Zhang, Xilin Ren, Zhongzhou Nuclear Theory We present a novel data-driven trap theory (abbreviated as DDTT) for nuclear scattering, which aims to overcome the limitations of the traditional trap method in dealing with narrow potential wells, while also providing a more efficient framework for handling long-range Coulomb interactions. As proof-of-concept examples, we employ this unified theory to analyze the elastic scattering of nucleon-nucleon and nucleon-α systems. DDTT can successfully produce results consistent with those from traditional approaches, highlighting its significance for ab initio light nuclei scattering studies and potential for applications in the heavier mass region. |
| title | Data-driven trap theory for nuclear scattering |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2509.11072 |