Automated Detection of Galactic Rings from Sloan Digital Sky Survey Images

Abstract Morphological features in galaxies—like spiral arms, bars, rings, and tidal tails, etc.—carry information about their structure, origin, and evolution. It is therefore important to catalog and study such features and to correlate them with other basic galaxy properties, the environments in which the galaxies are located, and their interactions with other galaxies. The volume of present and future data on galaxies is so large that traditional methods, which involve expert astronomers identifying morphological features through visual inspection, are no longer sufficient. It is therefore necessary to use AI-based techniques like machine learning and deep learning to find morphological structures quickly and efficiently. We report in this study the application of deep learning for finding ring-like structures in galaxy images from the Sloan Digital Sky Survey (SDSS) DR18. We use a catalog by R. J. Buta of ringed galaxies from SDSS to train the network, reaching good accuracy and recall, and generate a catalog of 29,420 galaxies, of which 4855 have ring-like structures with prediction confidence exceeding 90%. Using a catalog of barred galaxy images identified by S. Abraham et. al. with deep-learning techniques, we identify a set of 2087 galaxies with bars as well as rings. The catalog should be very useful in understanding the origin of these important morphological structures. As an example of the usefulness of the catalog, we explore the environments and star formation characteristics of the ring galaxies in our sample.