Improving Posterior Inference of Galaxy Properties with Image-Based Conditional Flow Matching

Abstract

Estimating physical properties of galaxies from wide-field surveys remains a central challenge in astrophysics. While spectroscopy provides precise measurements, it is observationally expensive, and photometry discards morphological information that correlates with mass, star formation history, metallicity, and dust. We present a conditional flow matching (CFM) framework that leverages pixel-level imaging alongside photometry to improve posterior inference of galaxy properties. Using $\sim10^5$ SDSS galaxies, we compare models trained on photometry alone versus photometry plus images. The image+photometry model outperforms the photometry-only model in posterior inference and more reliably recovers known scaling relations. Morphological information also helps mitigate the dust--age degeneracy. Our results highlight the potential of integrating morphology into photometric SED fitting pipelines, opening a pathway towards more accurate and physically informed constraints on galaxy properties.