Learning Nuclear Structure with AI: Radii and Collectivity
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...
| Authors | Giuliano Giacalone, Sokratis Trifinopoulos, Mike Williams |
|---|---|
| Primary category | nucl-th |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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