Searching for ultra-diffuse galaxies in the Dark Energy Survey using an object-detection algorithm

Ultra-diffuse galaxies (UDGs) are a class of galaxies characterized by an extremely low surface brightness and large effective radius. The significance of UDGs lies in their unique properties, such as their diverse dark-matter content, which collectively challenge existing theories of galaxy formation and evolution. However, their low surface brightness and diffuse stellar distributions make UDGs particularly difficult to detect. To address this challenge, this study introduces a deep learning-based object detection model for the Dark Energy Survey (DES), named UDGnet-DES. Combined with an iterative training strategy, this model is designed to conduct a large-scale search for UDG candidates in the DES Data Release 2 (DES DR2) imaging data. Using our model, we searched UDGs from more than 500 000 DES images and further filtered the results based on surface brightness and visual inspection. As a result, we obtained a catalog of 2991 UDG samples, 39 of which have spectroscopic redshifts. An analysis of our UDG samples reveals that most objects exhibit nearly circular morphologies. Among them, blue UDGs tend to have higher surface brightnesses, while red UDGs show significant spatial clustering, in contrast to the more uniformly distributed blue UDGs. The method developed in this study can improve the efficiency of UDG searches and will be applied to the search for UDGs in the China Space Station Telescope (CSST) survey project.