The nuclear symmetry energy from neutron skins and pure neutron matter in a Bayesian framework
William G. Newton, Gabriel Crocombe

TL;DR
This paper uses a Bayesian approach combining neutron skin measurements and theoretical neutron matter data to precisely infer key nuclear symmetry energy parameters, improving constraints significantly over previous methods.
Contribution
It introduces a Bayesian framework that integrates experimental neutron skin data with chiral effective field theory predictions to accurately determine symmetry energy parameters.
Findings
95% credible intervals for J, L, and K_{sym} parameters.
Neutron skin data alone can constrain symmetry energy parameters as effectively as theoretical calculations.
Predictions for neutron skin thicknesses of calcium and lead based on existing data.
Abstract
We present an inference of the nuclear symmetry energy magnitude , the slope and the curvature by combining neutron skin data on Ca, Pb and Sn isotopes and our best theoretical information about pure neutron matter (PNM). A Bayesian framework is used to consistently incorporate prior knowledge of the PNM equation of state from chiral effective field theory calculations. Neutron skins are modeled in a Hartree-Fock approach using an extended Skyrme energy-density functional which allows for independent variation of , and without affecting the symmetric nuclear matter equation of state. We discuss the choice of neutron skin data sets, and combining errors in quadrature we obtain 95\% credible values of MeV, MeV and MeV using…
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