Lin, ET, Hayes, F, Lamb, GP, Heng, IS, Kong, AKH, Williams, MJ, Saha, S and Veitch, J (2021) A bayesian inference framework for gamma-ray burst afterglow properties. Universe, 7 (9). p. 349.

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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.

Item Type:	Article
Subjects:	Q Science > QB Astronomy Q Science > QC Physics
Divisions:	Astrophysics Research Institute
Publisher:	MDPI AG
SWORD Depositor:	A Symplectic
Date Deposited:	13 Jun 2023 14:08
Last Modified:	13 Jun 2023 14:08
DOI or ID number:	10.3390/universe7090349
URI:	https://researchonline.ljmu.ac.uk/id/eprint/19789

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