3 papers
nucl-th2026
Optimizing artificial neural networks for dipole strength predictions in light nuclei
Tim Egert, Weiguang Jiang, Sonia Bacca
We present an optimized artificial neural network approach for predicting electric dipole strength functions in nuclei with . Building upon a previous global study [Phys.Re…
nucl-ex2026
Nuclear Charge Radius of Be from Muonic Atom Spectroscopy Using a Microcalorimeter
Ofir Eizenberg, Shikha Rathi, Andreas Abeln +34
The transition energy in muonic Be was measured using a metallic magnetic calorimeter, resulting in eV. The result is 30 times more pre…
nucl-th2024
Data-driven analysis of dipole strength functions using artificial neural networks
Weiguang Jiang, Tim Egert, Sonia Bacca +2
We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We tr…