Calibrating global behavior of equation of state by combining nuclear and astrophysics inputs in a machine learning approach

We implemented symbolic regression techniques to identify suitable analytical functions that map various properties of neutron stars (NSs), obtained by solving the Tolman-Oppenheimer-Volkoff (TOV) equations, to a few key parameters of the equation of state (EOS). These symbolic regression models (SRMs) are then employed to perform Bayesian inference with a comprehensive dataset from nuclear physics experiments and astrophysical observations. The posterior distributions of EOS parameters obtained from Bayesian inference using SRMs closely match those obtained directly from the solutions of TOV equations. Our SRM-based approach is approximately 100 times faster, enabling efficient Bayesian analyzes across different combinations of data to explore their sensitivity to various EOS parameters within a reasonably short time.