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Active galactic nuclei (AGN) are supermassive black holes with luminousaccretion disks found in some galaxies, and are thought to play an importantrole in galaxy evolution. However, traditional optical spectroscopy foridentifying AGN requires time-intensive observations. We train a convolutionalneural network (CNN) to distinguish AGN host galaxies from non-active galaxiesusing a sample of 210,000 Sloan Digital Sky Survey galaxies. We evaluate theCNN on 33,000 galaxies that are spectrally classified as composites, and findcorrelations between galaxy appearances and their CNN classifications, whichhint at evolutionary processes that affect both galaxy morphology and AGNactivity. With the advent of the Vera C. Rubin Observatory, Nancy Grace RomanSpace Telescope, and other wide-field imaging telescopes, deep learning methodswill be instrumental for quickly and reliably shortlisting AGN samples forfuture analyses.

Identifying AGN host galaxies with convolutional neural networks