Learning Nuclear Structure with AI: Radii and Collectivity

2026-09-15

AI Summary

本文通过开发基于NuCLR的多任务模型来研究原子核的电荷半径和电四极跃迁强度，以提供核结构的数据驱动基线，其性能优于单任务学习。

Abstract

Low-energy nuclear structure is encoded in a broad body of experimental information across the chart of nuclides. Learning how this information is organized across observables and nuclei can provide a data-driven empirical baseline for theoretical extrapolations and experimental design. Here, we develop held-out ensembles based on NuCLR (Nuclear Co-Learned Representations), a multi-task model of nuclear data, to study charge radii and electric-quadrupole transition strengths. Out-of-fold (OOF) validation shows that shared representation improves performance over single-task learning, yielding a charge-radius $\mathrm{RMS}$ deviation of $0.0147~{\rm fm}$ and a $\mathrm{B(E2)}$ $\mathrm{RMS}$ deviation of $0.192~e^2{\rm b}^2$ across hundreds of nuclides, competitive with state-of-the-art nuclear models. Our error bars estimate the expected prediction accuracy across the nuclear chart, highlighting regions where new data would encode information beyond the learned patterns. NuCLR thus serves as a data-driven surveyor of nuclear structure and a step toward a shared, multi-observable foundation model of the nuclear chart.

Problem

• nuclear structure
• charge radii
• electric-quadrupole transition strengths
• data-driven
• empirical baseline

Innovation

• NuCLR
• multi-task learning
• charge radii
• electric-quadrupole transition strengths
• nuclear structure