The impact of mass uncertainties on r-process nucleosynthesis in neutron star mergers

Context. Theoretically predicted yields of elements created by the rapid neutron capture (r-) process carry potentially large uncertainties associated with incomplete knowledge of nuclear properties and approximative hydrodynamical modeling of the matter ejection processes. One of the dominant uncertainties in determining the ejecta composition and radioactive decay heat stems from the still unknown nuclear masses of exotic neutron-rich nuclei produced during the neutron irradiation. Aims. We investigate both the model (systematic) and parameter (statistical) uncertainties affecting nuclear mass predictions and explore their impact on r-process production, and subsequently on the composition of neutron star merger ejecta. Methods. The impact of correlated model uncertainties on masses is estimated by considering five different nuclear mass models that are known to provide an accurate description of known masses. In addition, the uncorrelated uncertainties associated with local variation of model parameters are estimated using a variant of the backward-forward Monte Carlo method, to constrain the parameter changes to experimentally known masses before propagating them consistently to the unknown masses of neutron-rich nuclei. The impact of nuclear mass uncertainties is propagated to r-process nucleosynthesis in a 1.38-1.38 M-circle dot neutron star merger model considering a large and representative number of trajectories. Results. We find that the uncorrelated parameter uncertainties lead to ejected abundance uncertainties of 20% up to A similar or equal to 130 and 40% between A = 150 and 200, with peaks around A similar or equal to 140 and A similar or equal to 203 giving rise to deviations around 100-300%. The correlated model uncertainties remain larger than the parameter ones for most nuclei. However, both model and parameter uncertainties have an important impact on heavy nuclei production. Conclusions. Improvements to nuclear models are still crucial in reducing uncertainties in predictions related to r-process nucleosynthesis. Both correlated model uncertainties and coherently determining parameter uncertainties are key in the sensitivity analysis.