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. The 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\% precision score and an 85\% hit rate. Of 13,280 sources within GSA without an assigned transient classification our model predicts the CV class for $\sim$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.

16 pages, 8 figures, 8 tables

References in corpus (16)

• The Gaia mission
• Gaia Data Release 1. Summary of the astrometric, photometric, and survey properties
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• The All-Sky Automated Survey for Supernovae (ASAS-SN) Light Curve Server v1.0
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• PESSTO : survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects
• Machine Learning for the Zwicky Transient Facility
• Gaia Photometric Science Alerts
• A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline
• The ANTARES Astronomical Time-Domain Event Broker
• Intermediate Palomar Transient Factory: Realtime Image Subtraction Pipeline
• Discovery and characterization of five new eclipsing AM CVn systems
• Localised thermonuclear bursts from accreting magnetic white dwarfs
• MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition
• New Candidates for AM Canum Venaticorum Stars among ASAS-SN Transients
• Transient behavior of three SU UMa-type dwarf novae; AR Pic, QW Ser and V521 Peg