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.
- Publication:
-
RAS Techniques and Instruments
- Pub Date:
- January 2023
- DOI:
- 10.1093/rasti/rzad015
- arXiv:
- arXiv:2305.03629
- Bibcode:
- 2023RASTI...2..238W
- Keywords:
-
- machine learning;
- galaxies: nuclei;
- X-rays: galaxies;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - High Energy Astrophysical Phenomena
- E-Print:
- 18 pages. 16 figures