2025/12/04 by Mikaeel Yunus, John F. Wu, Yunus, Mikaeel +3 · 1 voice
Physics and Astronomy · #Astronomy and Astrophysical Research #Galaxies: Formation, Evolution, Phenomena #Gamma-ray bursts and supernovae #astro-ph.GA #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.2512.05078
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 ∼105 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.