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arXiv · 2509.11072

Data-driven trap theory for nuclear scattering

Hantao Zhang·Dong Bai·Xilin Zhang·Zhongzhou Ren
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Abstract

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-{\alpha} 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.

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Hantao Zhang, Dong Bai, Xilin Zhang, Zhongzhou Ren. 2025-09-14. Data-driven trap theory for nuclear scattering. https://arxiv.org/abs/2509.11072

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