  Besides their intrinsic nuclear-structure value, nuclear mass models are
essential for astrophysical applications, such as r-process nucleosynthesis and
neutron-star structure. To overcome the intrinsic limitations of existing
"state-of-the-art" mass models, we propose a refinement based on a Bayesian
Neural Network (BNN) formalism. A novel BNN approach is implemented with the
goal of optimizing mass residuals between theory and experiment. 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. 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.
