[论文] Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpret...

论文概要

研究领域: ML
作者: Phong Dang, Evander Espinoza, Xiaoliang Wan
发布时间: 2026-06-26
arXiv: 2606.28287

中文摘要

从头算建模已确立Wigner的SU(4)和Elliott的SU(3)作为轻核和中等质量核中核力的主导对称性。我们问它们是否也支配整个核图表中的核结合。我们的目标不是高精度预测，而是物理洞察，通过可解释的、基于对称性的模型。从SU(3)和SU(4) Casimir算符，我们构建三个神经网络（NN）质量模型：用于点预测的特征信息NN（FINN），添加不确定性量化的高斯信息NN（GINN），以及使用Casimir作为算符基的质量公式——Wigner信息NN（WINN）。所有模型都在AME2016上训练，并在AME2020的新核上验证。SU(4)算符单独将训练测试数据的均方根误差（RMSE）削减近一半，在推断上削减约五分之一，相对于液滴基线——表明Wigner对称性携带超越体性质的预测信息。尽管形式紧凑，WINN达到最低的验证RMSE，0.430 MeV——与最先进质量模型竞争。

原文摘要

Ab initio modeling has established Wigner's SU(4) and Elliott's SU(3) as dominant symmetries of the nuclear force in light and intermediate-mass nuclei. We ask whether they also govern nuclear binding across the entire chart. Our aim is not high-precision prediction but physical insight, through interpretable, symmetry-based models. From the SU(3) and SU(4) Casimir operators we construct three neural-network (NN) mass models: Feature-Informed NN (FINN) for point predictions, Gaussian-Informed NN (GINN) adding uncertainty quantification, and Wigner-Informed NN (WINN) -- a mass formula using the Casimirs as an operator basis. All are trained on AME2016 and validated on nuclei new to AME2020. The SU(4) operators alone cut the root-mean-square error (RMSE) by nearly half on train and test data...

自动采集于 2026-06-30

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