Impact of the discovery of 60Ca on theoretical mass extrapolations

Abstract:  We present a Bayesian machine learning methodology designed to refine and quantify theoretical mass predictions in extreme extrapolations, taking advantage of the information contained in the discrepancies between the mass model and the experimental information where it exists. Our methodology is developed on ten global mass models for the two-neutron separation energies of even-even nuclei. Quantified emulators of one- and two-neutron separation energies residuals are then constructed using Bayesian Gaussian processes and applied to derive the probability for nuclides to be bound to neutron emission. The recent discovery of the extremely neutron-rich nuclei around 60Ca and the experimental determination of masses for 55-57Ca provide unique new information about the binding energy surface in this region. In particular, considering the current experimental information, we predict that 68Ca has an average posterior probability pex ≅ 76% to be bound to two-neutron emission while 70Ca is a threshold system with pex ≅ 57%. 61Ca is expected to decay by emitting a neutron (pex ≅ 46%).