Speaker
Description
The comprehensive characterization of nuclear excited states is fundamental to our understanding of nuclear structure and is a cornerstone for modern applications. In this work, we introduce a robust machine learning (ML) framework designed for the global prediction of nuclear energy levels with high precision. Utilizing the Evaluated Nuclear Structure Data File (ENSDF) as a foundational dataset, we employ a data-driven approach to map spectral properties across the nuclear landscape. Despite using a sparse training set of only 20%, the model reproduces the remaining 80% of known levels with a mean deviation within 100 keV. The model demonstrates a capacity for extrapolation into presently unreachable regions of the nuclear chart, providing a predictive roadmap for future rare-isotope beam facilities.
| Contribution category | Theory |
|---|---|
| Presenter status | Faculty/Staff |