A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare compatibility models given observed data. However, describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten parameters are incorporated in model. With such complex model, likelihood evaluation for each Monte Carlo sampling point becomes massive task requires significant amount computational power. this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated This achieved by generating grid afterglow across space replacing with simple interpolation function high-dimensional that stores all curves. framework, compared original method, leads $\sim$90$\times$ speedup per estimation. It will allow us explore different jet enable fast model comparison future.