Bayesian uncertainty quantification of nuclear mass models for astrophysical rapid neutron capture process

The rapid neutron capture process (r-process) is believed to be responsible for the synthesis of the heaviest elements in the Universe and to occur in extreme astrophysical events such as compact binary mergers. Despite many efforts and developments to understand the workings of the r-process in nuclear physics and astrophysics, many challenges and unknowns remain. One of such challenges is to quantitatively gauge our understanding of the r-process, in other words, to obtain comprehensive uncertainty estimates in the theoretical reproduction of observable quantities.

Theoretical calculations of r-process abundance patterns are known to be sensitive to the choice of nuclear mass models, due to the dependence of reaction and decay rates on nuclear masses. In this talk, a method to quantify the uncertainty from the choice of mass model will be discussed. This Bayesian method allows for uncertainty quantification of deterministic mass models by probabilistically modeling an ensemble of commonly used mass models as a Gaussian mixture model, whose weights are inferred from experimental data through Bayes’ rule. The impact of the quantified mass uncertainty on the relevant nuclear reaction rates and the calculation of the r-process abundance pattern will also be discussed.