Machine learning-based search for cataclysmic variables within Gaia Science Alerts

Abstract

Wide-field time domain facilities detect transient events in large numbers through difference imaging. For example, Zwicky Transient Facility, produces alerts for hundreds of thousands of transient events per night, a rate set to be dwarfed by the upcoming Vera Rubin Observatory. The automation provided by Machine Learning (ML) is therefore necessary to classify these events and select the most interesting sources for follow-up observations. Cataclysmic Variables (CVs) are a transient class that are numerous, bright and nearby, providing excellent laboratories for the study of accretion and binary evolution. Here we focus on our use of ML to identify CVs from photometric data of transient sources published by the Gaia Science Alerts program (GSA) - a large, easily accessible resource, not fully explored with ML. Use of light curve feature extraction techniques and source metadata from the Gaia survey resulted in a Random Forest model capable of distinguishing CVs from supernovae, Active Galactic Nuclei and Young Stellar Objects with a 92 per cent precision score and an 85 per cent hit rate. Of 13,280 sources within GSA without an assigned transient classification our model predicts the CV class for ∼2800. Spectroscopic observations are underway to classify a statistically significant sample of these targets to validate the performance of the model. This work puts us on a path towards the classification of rare CV subtypes from future wide field surveys such as the Legacy Survey of Space and Time.

The ZTF Source Classification Project. III. A Catalog of Variable Sources

“…ML can be used to direct follow-up observations based on the latest survey data (e.g., Sravan et al 2023). ML techniques have also been applied to large surveys to classify different kinds of sources, including microlensing events (e.g., Godines et al 2019), transients (e.g., Stachie et al 2020;Gomez et al 2023;Rehemtulla & Miller 2023), and variables (e.g., Richards et al 2011;García-Jara et al 2022;Mistry et al 2022).…”

The GFCAT: A Catalog of Ultraviolet Variables Observed by GALEX with Subminute Resolution

“…New analytic capabilities, however, can allow us to reach into the archive and recover data from the past on scales of a century and down to resolutions of seconds. (Grindlay et al 2011;Mistry et al 2022) Also, due in part to the UV opacity of Earth's atmosphere, we have a much shallower corpus of data on the fast UV sky. This is unfortunate, because the UV is particularly interesting for the study of stellar phenomena.…”