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The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxiesduring its decade-long Legacy Survey of Space and Time. The rich imaging datait collects will be an invaluable resource for probing galaxy evolution acrosscosmic time, characterizing the host galaxies of transient phenomena, andidentifying novel populations of anomalous systems. To facilitate thesestudies, we introduce a convolutional variational autoencoder trained toestimate the redshift, stellar mass, and star-formation rates of galaxies frommulti-band imaging data. We train and test our physics-informed CVAE on aspectroscopic sample of $\sim$26,000 galaxies within $z<1$ imaged through theDark Energy Camera Legacy Survey. We show that our model can infer redshift andstellar mass more accurately than the latest image-based self-supervisedlearning approaches, and is >100x faster than more computationally-intensiveSED-fitting techniques. Using a small sample of Green Pea and Red Spiralgalaxies reported in the literature, we further demonstrate how this CVAE canbe used to rapidly identify rare galaxy populations and interpret what makesthem unique.

A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection