II Machine Learning Methods for Complex and Quantum Systems

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

Not scheduled

20m

Aula Magna (Facultat de Física, Universitat de Barcelona)

Poster

Description

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, we employed a Bayesian model averaging analysis based on Gaussian process emulators to extract the symmetry energy around $2\rho_0/3$ from the effective proton-neutron chemical potential difference $\Delta \mu^*_{\rm{pn}}$ of selected doubly magic nuclei.