Using Bayesian inference to model GRB afterglows

Using the powerful technique of Bayesian inference with dynamic nested sampling, I modeled a large sample of X-ray afterglow light curves observed by the Swift X-ray Telescope over 20 years (see here) using a range of models. I used the Bayes factors to compare competing jet models, and analysed the posteriors to infer the physical parameters. Now, with access to a large set of physical parameters, I am currently working on testing for a dependency of empirical correlations observed in GRB afterglows on these physical parameters. This could be the very first case of understanding the physical origin of such a correlation in GRB afterglows, though the analysis is a multifaceted problem and there are likely a combination of confounding parameters.