A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties

TL;DR

This paper introduces a fast Bayesian inference framework for gamma-ray burst afterglow modeling, significantly reducing computational costs and enabling rapid model comparison in multi-parameter physical systems.

Contribution

It presents an interpolated physical model approach that accelerates likelihood evaluation by approximately 90 times, facilitating efficient parameter estimation for complex GRB afterglow models.

Findings

Achieved ~90x speedup in likelihood evaluation.

Enabled rapid parameter estimation for simulated GRB light curves.

Facilitated future model comparison and exploration.

Abstract

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