Impact of nuclear mass models on $r$-process nucleosynthesis and heavy element abundances in $r$-process enhanced metal-poor stars

TL;DR

This paper evaluates how different nuclear mass models affect $r$-process nucleosynthesis predictions, finding that the WS4 model best matches observed heavy element abundances in metal-poor stars, highlighting its importance.

Contribution

The study systematically compares four nuclear physics models and demonstrates that the WS4 model provides the most accurate predictions for $r$-process nucleosynthesis in metal-poor stars.

Findings

WS4 model predictions closely match experimental data.

Heavy element abundances in stars are well reproduced by WS4-based simulations.

WS4 model is crucial for accurate $r$-process nucleosynthesis modeling.

Abstract

Due to the lack of experimental data on extremely neutron-rich nuclei, theoretical values derived from nuclear physics models are essential for the rapid neutron capture process ($r$-process). Metal-poor stars enriched by the $r$-process offer valuable cases for studying the impact of nuclear physics models on $r$-process nucleosynthesis. This study analyzes four widely used nuclear physics models in detail: Finite-Range Droplet Model, Hartree-Fock-Bogoliubov, Duflo-Zuker, and Weizs$\ddot{\rm a}$cker-Skyrme (WS4). Theoretical values predicted by the WS4 model are found to be in good agreement with experimental data, with deviations significantly smaller than those predicted by other models. The heavy element abundances observed in $r$-process enhanced metal-poor stars can be accurately reproduced by $r$-process nucleosynthesis simulations using the WS4 model, particularly for the rare earth elements. This suggests that nuclear data provided by nuclear physics model like WS4 are both essential and crucial for $r$-process nucleosynthesis studies.