Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach

Nuclear mass models provide essential input for astrophysical applications such as $r$-process nucleosynthesis and neutron-star structure. By using a Bayesian neural network formalism, the authors obtain a significant improvement of about 40% in the mass predictions, complemented with statistical errors, of existing models. From an average of these predictions a mass model is obtained that is used to predict the composition of the outer crust of a neutron star.