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

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YOU? · · 2025 · Open Access · · DOI: https://doi.org/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 $\sim10^5$ 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.

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Type: preprint
Landing Page: http://arxiv.org/abs/2512.05078
PDF: https://arxiv.org/pdf/2512.05078
OA Status: green
OpenAlex ID: https://openalex.org/W4417087255

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DOI: https://doi.org/10.48550/arxiv.2512.05078

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

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Type: preprint

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Publication year: 2025

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Publication date: 2025-12-04

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Authors: Mikaeel Yunus, John F. Wu

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Landing page: https://arxiv.org/abs/2512.05078

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PDF URL: https://arxiv.org/pdf/2512.05078

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