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

Understand

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.