  The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxies
during its decade-long Legacy Survey of Space and Time. The rich imaging data
it collects will be an invaluable resource for probing galaxy evolution across
cosmic time, characterizing the host galaxies of transient phenomena, and
identifying novel populations of anomalous systems. To facilitate these
studies, we introduce a convolutional variational autoencoder trained to
estimate the redshift, stellar mass, and star-formation rates of galaxies from
multi-band imaging data. We train and test our physics-informed CVAE on a
spectroscopic sample of $\sim$26,000 galaxies within $z<1$ imaged through the
Dark Energy Camera Legacy Survey. We show that our model can infer redshift and
stellar mass more accurately than the latest image-based self-supervised
learning approaches, and is >100x faster than more computationally-intensive
SED-fitting techniques. Using a small sample of Green Pea and Red Spiral
galaxies reported in the literature, we further demonstrate how this CVAE can
be used to rapidly identify rare galaxy populations and interpret what makes
them unique.
