Searching for Quasi-Periodic Eruptions using Machine Learning
arXiv:2305.03629 · doi:10.1093/rasti/rzad015
Abstract
Quasi-Periodic Eruptions (QPEs) are 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 lightcurves containing eruptions. Using a neural network and 14 variability measures we are able to classify lightcurves with accuracies of greater than 94% with simulated data and greater than 98% with observational data on a sample consisting of 12 lightcurves with QPEs and 52 lightcurves without QPEs. An analysis of 83,531 X-ray detections from the XMM Serendipitous Source Catalogue allowed us to recover lightcurves of known QPE sources and examples of several categories of variable stellar objects.
arXiv comment18 pages. 16 figures
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