  Active galactic nuclei (AGN) are supermassive black holes with luminous
accretion disks found in some galaxies, and are thought to play an important
role in galaxy evolution. However, traditional optical spectroscopy for
identifying AGN requires time-intensive observations. We train a convolutional
neural network (CNN) to distinguish AGN host galaxies from non-active galaxies
using a sample of 210,000 Sloan Digital Sky Survey galaxies. We evaluate the
CNN on 33,000 galaxies that are spectrally classified as composites, and find
correlations between galaxy appearances and their CNN classifications, which
hint at evolutionary processes that affect both galaxy morphology and AGN
activity. With the advent of the Vera C. Rubin Observatory, Nancy Grace Roman
Space Telescope, and other wide-field imaging telescopes, deep learning methods
will be instrumental for quickly and reliably shortlisting AGN samples for
future analyses.
