A Bayesian inference framework for gamma-ray burst afterglow properties

En Tzu Lin*, Fergus Hayes, Gavin P. Lamb, Ik Siong Heng, Albert K.H. Kong, Michael J. Williams, Surojit Saha, John Veitch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
Article number349
Number of pages8
JournalUniverse
Volume7
Issue number9
DOIs
Publication statusPublished - 17 Sept 2021

Keywords

  • Bayesian inference
  • GRB afterglows
  • Multi-messenger astronomy

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