pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population

Abstract

We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift $z\sim 6$. The model is trained on distributions of observed colors and magnitudes, from 26-band photometry of $\sim 420,000$ galaxies in the COSMOS2020 catalog with Spitzer IRAC $\text{Ch. 1}<26$. The generative model includes a flexible distribution over 16 stellar population synthesis (SPS) parameters, and a depth-dependent photometric uncertainty model, both represented using score-based diffusion models. We use the trained model to predict scaling relationships for the galaxy population, such as the stellar mass function, star-forming main sequence, and gas-phase and stellar metallicity vs. mass relations, demonstrating reasonable-to-excellent agreement with previously published results. We explore the connection between mid-infrared emission from active galactic nuclei (AGN) and star-formation rate, finding high AGN activity for galaxies above the star-forming main sequence at $1\lesssim z\lesssim 2$. Using the trained population model as a prior distribution, we perform inference of the redshifts and SPS parameters for 429,669 COSMOS2020 galaxies, including 39,588 with publicly available spectroscopic redshifts. The resulting redshift estimates exhibit minimal bias ($\text{median}[{\Delta }_z]=-8\times 10^{-4}$), scatter (${\sigma }_{\text{MAD}}=0.0132$), and outlier fraction ($6.19\%$) for the full $0<z<6$ spectroscopic compilation. These results establish that pop-cosmos can achieve the accuracy and realism needed to forward-model modern wide--deep surveys for Stage IV cosmology. We publicly release pop-cosmos software, mock galaxy catalogs, and COSMOS2020 redshift and SPS parameter posteriors.

47 pages, 34 figures. Accepted for publication in ApJ. Mock galaxy catalogs available at https://doi.org/10.5281/zenodo.15757717 . MCMC chains available at https://doi.org/10.5281/zenodo.15850536 . Software available at https://github.com/Cosmo-Pop

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