Uniform priors are not as uninformative as you might think

Characterising the Variability of the Black Hole at the Centre of our Galaxy using Multi-Output Gaussian Processes

The advent of large-scale surveys that observe thousands, if not millions, of sources puts the task of identifying novel candidates beyond the manual capability of astronomers. To automate the sifting of these sources at scale, a statistical approach can be used to characterise and classify promising candidates. Care must be taken, however, to not overfit to a particular object type or survey configuration so that processing can be applied across surveys without introducing statistical biases. In this work, I present a Gaussian process (GP) regression approach for statistically characterising light curves and demonstrate this approach on 5131 radio light curves from the ThunderKAT survey. The benefits of a GP approach include the implicit handling of data sparsity and irregular sampling, accommodating light curves of diverse shapes, and having astrophysically meaningful interpretations of the fitted model hyperparameters. Indeed, I found distinct regions in the amplitude hyperparameter space that point to a candidate's propensity to be a transient or variable source. Compared with variability metrics commonly used in radio astronomy, this approach has improved discriminatory power.