Propagation of statistical uncertainties of Skyrme mass models to simulations of $r$-process nucleosynthesis

T. M. Sprouse, R. Navarro Perez, R. Surman, M. Mumpower, G. C. McLaughlin, N. Schunck

Unpublished submitted (2019)

Uncertainties in nuclear models have a major impact on simulations that aim at understanding the origin of heavy elements in the universe through the rapid neutron capture process ($r$ process) of nucleosynthesis. Within the framework of the nuclear density functional theory, we use results of Bayesian statistical analysis to propagate uncertainties in the parameters of energy density functionals to the predicted r-process abundance pattern, by way not only of the nuclear masses but also through the influence of the masses on $\beta$-decay and neutron capture rates. We additionally make the first identifications of specific parameters of Skyrme-like energy density functionals which are correlated with particular aspects of the r-process abundance pattern. While previous studies have explored the reduction in the abundance pattern uncertainties due to anticipated new measurements of neutron-rich nuclei, here we point out that an even larger reduction will occur when these new measurements are used to reduce the uncertainty of model predictions of masses, which are then propagated through to the abundance pattern. We make a quantitative prediction for how large this reduction will be.



nuclear theory Notre Dame LANL

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