A Brief Introduction to BayesFlare

BayesFlare was created to provide an automated means of identifying flaring events in light curves released by the Kepler mission. The aim was to provide a technique that was able to identify even weak events by making use of the flare signal shape. This has led to the modern package containing functions to perform Bayesian hypothesis testing comparing the probability of light curves containing flares to that of them containing noise (or non-flare-like) artefacts.

The statistical methods used in BayesFlare owe much to data analysis developments from the field of gravitational wave research; the detection statistic which is used is based on one developed to identify ring-downs signals from neutron stars in gravitational wave detector data [1].

During the development of the analysis a method was found to account for underlying sinusoidal variations in light curve data by including such variations in the signal model, and then analytically marginalising over them. The functions to do this have also been included in the amplitude-marginaliser suite.


[1]Clark, Heng, Pitkin and Woan, PRD, 043003 (2007), arXiv:gr-qc/0703138