Microscopic derivation of the interacting boson model parameters with machine learning

Abstract

Machine learning is applied to derive microscopically parameters of the interacting boson model for nuclear spectroscopy. A physics-guided neural network is proposed, which is trained to map the potential energy landscapes that are calculated within the nuclear density functional theory onto the bosonic parameter space. To incorporate the underlying nuclear structure information and mitigate parameter degeneracy, the network integrates a global quadrupole collectivity indicator and valence nucleon numbers as key input features. In its applications to rare-earth nuclei, by reproducing the microscopic energy landscapes without any manual parameter tuning, the trained network is shown to provide a set of the model parameters and energy spectra that reflect the nuclear structural evolution, offering a robust alternative microscopic description of nuclear collectivity.

arXiv comment7 pages, 5 figures

References in corpus (9)

• Machine Learning in Nuclear Physics
• Mean field and beyond description of nuclear structure with the Gogny force: A review
• Taming nuclear complexity with a committee of multilayer neural networks
• Robust Regularity in γ-Soft Nuclei and its Microscopic Realization
• Neural Network Emulation of Spontaneous Fission
• Empirical physical formula for potential energy curves of 38-66Ti isotopes by using neural networks
• Shape transition of Nd and Sm isotopes and neutrinoless double-beta decay nuclear matrix element of ${}^{150}$Nd
• Mapped interacting boson model for nuclear structure studies
• Configuration mixing effects on neutrinoless $\beta \beta$-decay nuclear matrix elements