Abstract
Background: Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as 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.
- Received 25 August 2015
- Revised 14 December 2015
DOI:https://doi.org/10.1103/PhysRevC.93.014311
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