PopSED: Population-Level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation

TL;DR

PopSED is a neural density estimation framework that efficiently infers galaxy population properties directly from broadband photometry, significantly speeding up analysis compared to traditional methods.

Contribution

We introduce PopSED, a novel population-level inference method using normalizing flows and Wasserstein distance, enabling rapid analysis of galaxy populations from photometric data.

Findings

Accurately recovers galaxy redshift and stellar mass distributions

Achieves analysis speed 10^5 to 10^6 times faster than traditional SED modeling

Recovers the star-forming main sequence for GAMA galaxies at z<0.1

Abstract

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.