Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei

Abstract

The data-driven Bayesian model averaging is a rigorous statistical approach to combining multiple models for a unified prediction. Compared with the individual model, it provides more reliable information, especially for problems involving apparent model dependence. In this work, within both the non-relativistic Skyrme energy density functional and the nonlinear relativistic mean field model, the effective proton-neutron chemical potential difference Δμpn⁎ of neutron-rich nuclei is found to be strongly sensitive to the symmetry energy Esym(ρ) around 2ρ0/3, with ρ0 being the nuclear saturation density. Given discrepancies on the Δμpn⁎-Esym(2ρ0/3) correlations between the two models, we carried out a Bayesian model averaging analysis based on Gaussian process emulators to extract the symmetry energy around 2ρ0/3 from the measured Δμpn⁎ of 5 doubly magic nuclei 48Ca, 68Ni, 88Sr, 132Sn and 208Pb. Specifically, the Esym(2ρ0/3) is inferred to be Esym(2ρ0/3)=25.6−1.3+1.4MeV at 1σ confidence level. The obtained constraints on the Esym(ρ) around 2ρ0/3 agree well with microscopic predictions and results from other isovector indicators.