Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks

תקציר מקורי באנגליתarXiv:2606.28287v2 Announce Type: replace-cross Abstract: Ab initio theory establishes Wigner's SU(4) and Elliott's SU(3) as dominant symmetries of the nuclear force in light and intermediate-mass nuclei. Previous work shows the relevance of the former symmetry for nuclear binding, whether the latter organizes binding remains elusive. We probe whether both these symmetries organize nuclear masses, aiming at physical insights through interpretable models and predictive capability. From the SU(3) and SU(4) Casimirs we build 3 neural-network models. Two are conventional, a feature-informed NN (FINN) and a Gaussian variant (GINN) with predictive spread, while Wigner-informed network (WINN) is a new design constraining the mass formula to be linear in the operators, learning their (N,Z)-depende