2025/09/09 by Goode, Simon R., Webb, Sara A., Cooke, Jeff +12
#FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · doi:10.48550/arxiv.2509.07592
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∼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+6.39-3.28×105 per day for these sub-minute transient candidates.