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

TL;DR

This paper introduces a conditional flow matching framework that combines galaxy images and photometry to enhance the accuracy of posterior inference of galaxy properties, outperforming models that use only photometry.

Contribution

The novel framework integrates pixel-level imaging with photometry for improved galaxy property inference, addressing limitations of traditional methods.

Findings

Image+photometry model outperforms photometry-only in posterior accuracy.

Morphological info helps reduce dust--age degeneracy.

Framework improves recovery of galaxy scaling relations.

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.