A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme
Simon R. Goode, Sara A. Webb, Jeff Cooke, Jielai Zhang, James Freeburn, Amy Lien, Mohsen Shamohammadi, Alexandra Rosenthal, Laura N. Driessen, Christopher Fluke, Ashish Mahabal, Anais Möller, Dougal Dobie, Adam Batten, Natasha Van Bemmel
Published: 2025/9/9
Abstract
Optical transient surveys continue to generate increasingly large datasets, prompting the introduction of machine-learning algorithms to search for quality transient candidates efficiently. Existing machine-learning infrastructure can be leveraged in novel ways to search these datasets for new classes of transients. We present a machine-learning accelerated search pipeline for the Deeper, Wider, Faster (DWF) programme designed to identify high-quality astrophysical transient candidates that contain a single detection. Given the rapid observing cadence of the DWF programme, these single-detection transient candidates have durations on sub-minute timescales. This work marks the first time optical transients have been systematically explored on these timescales, to a depth of m$\sim$23. We report the discovery of two high-quality sub-minute transient candidates from a pilot study of 671,761 light curves and investigate their potential origins with multiwavelength data. We discuss, in detail, possible non-astrophysical false positives, confidently reject electronic artefacts and asteroids, ruling out glints from satellites below 800 km and strongly disfavouring those at higher altitudes. We calculate a rate on the sky of $4.72^{+6.39}_{-3.28}\times10^5$ per day for these sub-minute transient candidates.