Background: Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure.Purpose: To overcome the intrinsic limitations of existing "state-of-the-art" mass models through a refinement based on a Bayesian Neural Network (BNN) formalism.Methods: A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment.Results: A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are now accompanied by proper statistical errors. Finally, by constructing a "world average" of these predictions, a mass model is obtained that is used to predict the composition of the outer crust of a neutron star.
Conclusions:The power of the Bayesian neural network method has been successfully demonstrated by a systematic improvement in the accuracy of the predictions of nuclear masses. Extension to other nuclear observables is a natural next step that is currently under investigation.