  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.
