Ultra-diffuse galaxies in the Kilo-Degree Survey with deep learning

Ultra-diffuse galaxies (UDGs) are a subset of low-surface-brightness galaxies (LSBGs), showing mean effective surface brightness fainter than 24 ̊m mag ̊m -2 and a diffuse morphology, with effective radii larger than 1.5 kpc. Due to their elusiveness, using traditional methods over large sky areas is challenging. Here we present a catalog of UDG candidates identified in the full 1350 deg2$ area of the Kilo-Degree Survey (KiDS) using deep learning. In particular, we used a previously developed network for the detection of low-surface-brightness systems in the Sloan Digital Sky Survey (SBGnet) and optimized for UDG detection. We trained this new UDG detection network for KiDS (UDGnet-K), with an iterative approach, starting from a small-scale training sample. After training and validation, the UGDnet-K has been able to identify $ UDG candidates, of which, after visual inspection, we selected 545 high-quality ones. The catalog contains the independent rediscovery of previously confirmed UDGs in local groups and clusters (e.g., NGC 5846 and Fornax), and new discovered candidates in about 15 local systems, for a total of 67 bona fide associations. Besides the value of the catalog per se for future studies of UDG properties, this work shows the effectiveness of an iterative approach to training deep learning tools in presence of poor training samples, due to the paucity of confirmed UDG examples, which we expect to replicate for upcoming all-sky surveys such as those by the Rubin Observatory, Euclid, and the China Space Station Telescope.