Searching for quasi-periodic eruptions using machine learning

Abstract

Quasi-periodic eruption (QPE) is a rare phenomenon in which the X-ray emission from the nuclei of galaxies shows a series of large amplitude flares. Only a handful of QPEs have been observed but the possibility remains that there are as yet undetected sources in archival data. Given the volume of data available a manual search is not feasible, and so we consider an application of machine learning to archival data to determine whether a set of time-domain features can be used to identify further light curves containing eruptions. Using a neural network and 14 variability measures we are able to classify light curves with accuracies of greater than $94{{\ \rm per\ cent}}$ with simulated data and greater than $98{{\ \rm per\ cent}}$ with observational data on a sample consisting of 12 light curves with QPEs and 52 light curves without QPEs. An analysis of 83 531 X-ray detections from the XMM Serendipitous Source Catalogue allowed us to recover light curves of known QPE sources and examples of several categories of variable stellar objects.