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Understanding the formation and evolution of ring galaxies, galaxies with anatypical ring-like structure, will improve understanding of black holes andgalaxy dynamics as a whole. Current catalogs of rings are extremely limited:manual analysis takes months to accumulate an appreciable sample of rings andexisting computational methods are vastly limited in terms of accuracy anddetection rate. Without a sizable sample of rings, further research into theirproperties is severely restricted. This project investigates the usage of aconvolutional neural network (CNN) to identify rings from unclassified samplesof galaxies. A CNN was trained on a sample of 100,000 simulated galaxies,transfer learned to a sample of real galaxies and applied to a previouslyunclassified dataset to generate a catalog of rings which was then manuallyverified. Data augmentation with a generative adversarial network (GAN) tosimulate images of galaxies was also used. A catalog of 1151 rings wasextracted with 7.4 times the precision and 15.4 times the detection rate ofconventional algorithms. The properties of these galaxies were then estimatedfrom their photometry and compared to the Galaxy Zoo 2 catalog of rings. Withupcoming surveys such as the Vera Rubin Observatory Legacy Survey of Space andTime obtaining images of billions of galaxies, similar models could be crucialin classifying large populations of rings to better understand the peculiarmechanisms by which they form and evolve.

Analysis of Ring Galaxies Detected Using Deep Learning with Real and Simulated Data