A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties
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 90 speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future.
Cite
@article{arxiv.2109.14993,
title = {A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties},
author = {En-Tzu Lin and Fergus Hayes and Gavin P. Lamb and Ik Siong Heng and Albert K. H. Kong and Michael J. Williams and Surojit Saha and John Veitch},
journal= {arXiv preprint arXiv:2109.14993},
year = {2021}
}
Comments
9 pages, 4 figures, accepted to the special issue of Universe, "Waiting for GODOT -- Present and Future of Multi-Messenger Astronomy"