Ultra-diffuse galaxies from DES DR2

The significance of UDGs lies in their unique properties, such as their diverse dark matter content, which 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 UDGnet-DES, a deep learning-based object detection model, combined with an iterative training strategy, to conduct a large-scale search for UDG candidates in the Dark Energy Survey (DES) DR2 imaging data. Using our model, we searched UDGs from more than 500000 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, of which 39 have spectroscopic redshifts.