  We present PopSED, a framework for the population-level inference of galaxy
properties from photometric data. Unlike the traditional approach of first
analyzing individual galaxies and then combining the results to determine the
physical properties of the entire galaxy population, we directly make the
population distribution the inference objective. We train normalizing flows to
approximate the population distribution by minimizing the Wasserstein distance
between the synthetic photometry of the galaxy population and the observed
data. We validate our method using mock observations and apply it to galaxies
from the GAMA survey. PopSED reliably recovers the redshift and stellar mass
distribution of $10^{5}$ galaxies using broadband photometry within $<1$ GPU
hr, being $10^{5-6}$ times faster than the traditional spectral energy
distribution modeling method. From the population posterior, we also recover
the star-forming main sequence for GAMA galaxies at $z<0.1$. With the
unprecedented number of galaxies in upcoming surveys, our method offers an
efficient tool for studying galaxy evolution and deriving redshift
distributions for cosmological analyses.
