Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei
arXiv:2312.07031 · doi:10.1016/j.physletb.2023.138435
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 of neutron-rich nuclei is found to be strongly sensitive to the symmetry energy around , with being the nuclear saturation density. Given discrepancies on the - correlations between the two models, we carry out a Bayesian model averaging analysis based on Gaussian process emulators to extract the symmetry energy around from the measured of 5 doubly magic nuclei Ca, Ni, Sr, Sn and Pb. Specifically, the is inferred to be at confidence level. The obtained constraints on the around agree well with microscopic predictions and results from other isovector indicators.
6 pages, 4 figures; published version
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