  Understanding the formation and evolution of ring galaxies, galaxies with an
atypical ring-like structure, will improve understanding of black holes and
galaxy dynamics as a whole. Current catalogs of rings are extremely limited:
manual analysis takes months to accumulate an appreciable sample of rings and
existing computational methods are vastly limited in terms of accuracy and
detection rate. Without a sizable sample of rings, further research into their
properties is severely restricted. This project investigates the usage of a
convolutional neural network (CNN) to identify rings from unclassified samples
of 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 previously
unclassified dataset to generate a catalog of rings which was then manually
verified. Data augmentation with a generative adversarial network (GAN) to
simulate images of galaxies was also used. A catalog of 1151 rings was
extracted with 7.4 times the precision and 15.4 times the detection rate of
conventional algorithms. The properties of these galaxies were then estimated
from their photometry and compared to the Galaxy Zoo 2 catalog of rings. With
upcoming surveys such as the Vera Rubin Observatory Legacy Survey of Space and
Time obtaining images of billions of galaxies, similar models could be crucial
in classifying large populations of rings to better understand the peculiar
mechanisms by which they form and evolve.
