Nuclear masses learned from a probabilistic neural network

Abstract

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