The JWST Extragalactic Mock Catalog: Modeling galaxy populations from the UV through the near-IR over thirteen billion years of cosmic history
Christina C. Williams, Emma Curtis-Lake, Kevin N. Hainline, Jacopo, Chevallard, Brant E. Robertson, Stephane Charlot, Ryan Endsley, Daniel P., Stark, Christopher N.A. Willmer, Stacey Alberts, Ricardo Amorin, Santiago, Arribas, Stefi Baum, Andrew Bunker, Stefano Carniani

TL;DR
This paper introduces a comprehensive phenomenological model for galaxy evolution from redshift 0.2 to 15, producing mock catalogs to aid future JWST surveys and improve understanding of high-redshift galaxy populations.
Contribution
It presents a novel, self-consistent model that simulates galaxy properties across cosmic history, incorporating stellar, gas, and dust emission, and provides publicly available mock catalogs for JWST survey planning.
Findings
Predicts thousands of galaxies at z>6 detectable by JWST NIRCam.
Forecasts tens of galaxies at z>10 within the JADES survey.
Enables constraints on UV luminosity function evolution at z>8.
Abstract
We present an original phenomenological model to describe the evolution of galaxy number counts, morphologies, and spectral energy distributions across a wide range of redshifts (0.2<z<15) and stellar masses [Log10 M/Msun >6]. Our model follows observed mass and luminosity functions of both star-forming and quiescent galaxies, and reproduces the redshift evolution of colors, sizes, star-formation and chemical properties of the observed galaxy population. Unlike other existing approaches, our model includes a self-consistent treatment of stellar and photoionized gas emission and dust attenuation based on the BEAGLE tool. The mock galaxy catalogs generated with our new model can be used to simulate and optimize extragalactic surveys with future facilities such as the James Webb Space Telescope (JWST), and to enable critical assessments of analysis procedures, interpretation tools, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
