COSMOS2025: A Machine Learning Census of Massive Quiescent Galaxies at 2.5 < z < 5
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
astro-ph.GA
Submitted: 2026-07-14
Comments: 17 pages, 12 figures, 6 tables, Accepted for Publication in ApJ
Journal ref: The Astrophysical Journal, 1007:60 (14pp), 2026 August 10
Code: https://github.com/vahidoo7/ML-High-redshift-Quiescent-Galaxy-Identifier
License: http://creativecommons.org/publicdomain/zero/1.0/
The gist: The existence of massive quiescent galaxies at high redshifts (z 2) strongly constrains the rapid quenching mechanisms in galaxy evolution models.
Terminology
Abstract
The existence of massive quiescent galaxies at high redshifts (z 2) strongly constrains the rapid quenching mechanisms in galaxy evolution models. We present a machine learning framework to identify massive ((M*/M) > 9.5) quiescent galaxies at 2.5 < z < 5 in the COSMOS2025 catalog. We train a CatBoostClassifier on mock photometry from the Santa Cruz semi-analytic models (SAMs), incorporating key JWST NIRCam bands and realistic noise to transfer the SAM-derived quiescent label (based on specific star-formation rate) to the observational space. When validated against the SAM ground truth, our classifier achieves a significantly higher recall (completeness) of 78% (compared to 53% for spectral energy distribution (SED)-fitting), while maintaining a high purity of 82%. Applied to the COSMOS2025 sample, and assuming the SAM definition of quiescence transfers to the real Universe, the model identifies 1111 quiescent candidates, a population 2.6 times larger than the 427 candidates identified via the catalog's simple SED-fitting configuration. Under the SAM definition of quiescence, this consistent pattern of high purity but poor completeness suggests that the SED-fitting methods, constrained by simplified parametric star-formation histories, may miss a significant fraction of the quiescent population, likely galaxies in crucial transitional evolutionary stages. The trained classifier and classified COSMOS2025 sample are publicly available.
Sources
- Euclid Definition Study Report
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
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