A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties
1
Institute of Astronomy, National Tsing Hua University, Hsinchu 300044, Taiwan
2
SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK
3
School of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Lijing Shao
Universe 2021, 7(9), 349; https://doi.org/10.3390/universe7090349
Received: 2 September 2021
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Revised: 15 September 2021
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Accepted: 15 September 2021
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Published: 17 September 2021
(This article belongs to the Special Issue Waiting for GODOT—Present and Future of Multi-Messenger Astronomy)
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.
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MDPI and ACS Style
Lin, E.-T.; Hayes, F.; Lamb, G.P.; Heng, I.S.; Kong, A.K.H.; Williams, M.J.; Saha, S.; Veitch, J. A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties. Universe 2021, 7, 349. https://doi.org/10.3390/universe7090349
AMA Style
Lin E-T, Hayes F, Lamb GP, Heng IS, Kong AKH, Williams MJ, Saha S, Veitch J. A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties. Universe. 2021; 7(9):349. https://doi.org/10.3390/universe7090349
Chicago/Turabian StyleLin, En-Tzu, Fergus Hayes, Gavin P. Lamb, Ik Siong Heng, Albert K. H. Kong, Michael J. Williams, Surojit Saha, and John Veitch. 2021. "A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties" Universe 7, no. 9: 349. https://doi.org/10.3390/universe7090349
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