Searching for Quasi-Periodic Eruptions using Machine Learning

2023/05/05 by Robbie Webbe, Webbe, Robbie, A. J. Young +1 · 2 citations
Engineering · Physics and Astronomy · #SAS software applications and methods #Astronomical Observations and Instrumentation #Gamma-ray bursts and supernovae

paper · pdf · doi:10.48550/arxiv.2305.03629

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.

Cited by

• Needle in a Poissonian haystack -- An X-ray astronomer's guide to QPE fishing
• Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves

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