Nuclear masses learned from a probabilistic neural network

Modeling of nuclear masses is important for many areas science including astrophysics, reaction modeling, and data evaluations, but accuracy challenging. This paper shows how judicious use physics knowledge---so-called feature-space engineering---in machine learning, coupled with sophisticated models theoretical uncertainties, can lead to better predictions.