pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
Stephen Thorp, Hiranya V. Peiris, Gurjeet Jagwani, Sinan Deger, Justin Alsing, Boris Leistedt, Daniel J. Mortlock, Anik Halder, Joel Leja

TL;DR
This paper introduces pop-cosmos, a generative model trained on extensive galaxy data to accurately simulate galaxy populations, infer properties, and predict relationships up to redshift 6, aiding future cosmological surveys.
Contribution
The paper presents a novel deep generative model for galaxy populations that incorporates 16 SPS parameters and photometric uncertainties, enabling realistic simulations and precise redshift inference.
Findings
Accurately predicts galaxy scaling relations up to z~6.
Achieves minimal bias and scatter in redshift estimates.
Provides publicly available software and catalogs for community use.
Abstract
We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift . The model is trained on distributions of observed colors and magnitudes, from 26-band photometry of galaxies in the COSMOS2020 catalog with Spitzer IRAC . 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…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research
