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A bayesian inference framework for gamma-ray burst afterglow properties

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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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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