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

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.