Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semisupervised Deep Learning

Abstract The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semisupervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. We selected over 5000 confirmed ringed galaxies from the Catalog of Southern Ringed Galaxies and the Northern Ringed Galaxies from the GZ2 catalog (GZ2-CNRG), both verified by morphology expert R. J. Buta, to create an annotated training set. Additionally, we incorporated strictly selected nonringed galaxy samples from the Galaxy Zoo 2 data set and utilized unlabeled data from the DESI Legacy Surveys to train our model. Through semisupervised learning, the model significantly reduced reliance on extensive annotated data while enhancing robustness and generalization. On the test set, it demonstrated exceptional performance in identifying ringed galaxies. With a probability threshold of 0.5, the classification accuracy reached 97%, with precision and recall for ringed galaxies at 94% and 93%, respectively. Building on these results, we predicted 750,000 galaxy images from the DESI Legacy Imaging Surveys with r -band apparent magnitudes less than 17.0 and redshifts in the range 0.0005 < z < 0.25, compiling the largest catalog of ringed galaxies to date, containing 62,962 galaxies with ring structures. This catalog provides essential data for subsequent research on the formation mechanisms and evolutionary history of galaxy rings.