Benchmarking artificial neural network extrapolations of the ground-state energies and radii of Li isotopes

We present a comparison of model-space extrapolation methods for No-Core Shell Model calculations of ground-state energies and root-mean-square radii in Li isotopes. In particular, we benchmark the latest machine-learning tools against widely used exponential and infrared extrapolations for energies and crossing point estimates for radii. Our findings demonstrate that, while some machine-learning-based approaches provide reliable predictions with robust statistical uncertainties for both observables even in small model spaces, others perform less consistently. The predictions of the former are compatible with established exponential and infrared extrapolations for energies while also providing a notable improvement over conventional radius estimates.