Microscopically constrained mean field models from chiral nuclear thermodynamics

TL;DR

This paper assesses mean field models' ability to approximate microscopic nuclear equations of state from chiral effective field theory, aiming to improve astrophysical simulations of phenomena like supernovae and neutron star mergers.

Contribution

It compares various mean field models with microscopic chiral nuclear forces, identifying models that reliably reproduce nuclear thermodynamics across relevant densities and temperatures.

Findings

Selected mean field models match microscopic thermodynamics within uncertainties.

Quantum Monte Carlo determines valid density regimes for perturbation theory.

Mean field models extend applicability in astrophysical simulations.

Abstract

We explore the use of mean field models to approximate microscopic nuclear equations of state derived from chiral effective field theory across the densities and temperatures relevant for simu- lating astrophysical phenomena such as core-collapse supernovae and binary neutron star mergers. We consider both relativistic mean field theory with scalar and vector meson exchange as well as energy density functionals based on Skyrme phenomenology and compare to thermodynamic equa- tions of state derived from chiral two- and three-nucleon forces in many-body perturbation theory. Quantum Monte Carlo simulations of symmetric nuclear matter and pure neutron matter are used to determine the density regimes in which perturbation theory with chiral nuclear forces is valid. Within the theoretical uncertainties associated with the many-body methods, we find that select mean field models describe well microscopic nuclear thermodynamics. As an additional consistency requirement, we study as well the single-particle properties of nucleons in a hot/dense environment, which affect e.g., charged-current weak reactions in neutron-rich matter. The identified mean field models can be used across a larger range of densities and temperatures in astrophysical simulations than more computationally expensive microscopic models.