Nuclear symmetry energy from neutron skins and pure neutron matter in a Bayesian framework

We present an inference of the nuclear symmetry energy magnitude $J$, the slope $L$ and the curvature $K_{\rm sym}$ 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 $J$, $L$ and $K_{\rm sym}$ 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 $J=31.3\substack{+4.2 \\ -5.9}$ MeV, $L=40\substack{+34 \\ -26}$ MeV and $K_{\tau} = L - 6K_{\rm sym}= -444\substack{+100 \\ -84}$ MeV using uninformative priors in $J$, $L$ and $K_{\rm sym}$, and $J=31.9\substack{+1.3 \\ -1.3}$ MeV, $L=37\substack{+9 \\ -8}$ MeV and $K_{\tau} = -480\substack{+25 \\ -26}$ MeV using PNM priors. The correlations between symmetry energy parameters induced by neutron skin data is discussed and compared with the droplet model. Neutron skin data alone is shown to place limits on the symmetry energy parameters as stringent as those obtained from chiral effective field theory alone, and when combined the 95\% credible intervals are reduced by a factor of 4-5. Ahead of new measurements of lead and calcium neutron skins from parity-violating electron scattering experiments at Jefferson Lab and Mainz Superconducting Accelerator, we make predictions based on existing data on neutron skins of tin for the neutron skins of calcium and lead of 0.166$\pm$0.008 fm and $0.169 \pm 0.014$ fm respectively, using uninformative priors, and 0.167$\pm$0.008 fm and $0.172 \pm 0.015$ fm respectively, using PNM priors.