  The mass, or binding energy, is the basis property of the atomic nucleus. It
determines its stability, and reaction and decay rates. Quantifying the nuclear
binding is important for understanding the origin of elements in the universe.
The astrophysical processes responsible for the nucleosynthesis in stars often
take place far from the valley of stability, where experimental masses are not
known. In such cases, missing nuclear information must be provided by
theoretical predictions using extreme extrapolations. Bayesian machine learning
techniques can be applied to improve predictions by taking full advantage of
the information contained in the deviations between experimental and calculated
masses. We consider 10 global models based on nuclear Density Functional Theory
as well as two more phenomenological mass models. The emulators of S2n
residuals and credibility intervals defining theoretical error bars are
constructed using Bayesian Gaussian processes and Bayesian neural networks. We
consider a large training dataset pertaining to nuclei whose masses were
measured before 2003. For the testing datasets, we considered those exotic
nuclei whose masses have been determined after 2003. We then carried out
extrapolations towards the 2n dripline. While both Gaussian processes and
Bayesian neural networks reduce the rms deviation from experiment
significantly, GP offers a better and much more stable performance. The
increase in the predictive power is quite astonishing: the resulting rms
deviations from experiment on the testing dataset are similar to those of more
phenomenological models. The empirical coverage probability curves we obtain
match very well the reference values which is highly desirable to ensure
honesty of uncertainty quantification, and the estimated credibility intervals
on predictions make it possible to evaluate predictive power of individual
models.
