Data-driven trap theory for nuclear scattering
Hantao Zhang, Dong Bai, Xilin Zhang, Zhongzhou Ren
公開日: 2025/9/14
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.