AGNBoost: A Machine Learning Approach to Active Galactic Nuclei Identification with JWST/NIRCam+MIRI Colors and Photometry
Year: 2026
Type: article
Abstract: We present AGNBoost , a machine learning framework utilizing XGBoostLSS to identify active galactic nuclei (AGNs) and estimate redshifts from James Webb Space Telescope (JWST) Near Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI) photometry. AGNBoost constructs 66 input features from seven NIRCam and four MIRI bands to predict the fraction of mid-IR 3–30 μ m emission attributable to an AGN power law (frac AGN ) and photometric redshift. Each model is trained on 10 6 simulated galaxies from the Code Investigating GALaxy Emission ( CIGALE ). Models are tested on mock CIGALE galaxies, an independent set of empirically derived templates, and 748 observations from the JWST MIRI EGS Galaxy and AGN (MEGA) survey. On idealized noise-free mock CIGALE galaxies, AGNBoost achieves 15% outlier fractions of 1.63% (frac AGN ) and 0.15% (redshift), with σ RMSE = 0.045 for frac AGN and σ NMAD = 0.004 for redshift. When realistic photometric uncertainties are introduced, performance remains robust with median predictions lying on the 1:1 relation, though outlier fractions increase to 4.38% and 3.35%, respectively. On the independent template set, AGNBoost identifies 92.6% of AGN candidates with frac AGN > 0.3 and 100% with frac AGN > 0.5, demonstrating generalization beyond the training distribution. For MEGA galaxies with spectroscopic redshifts, AGNBoost achieves σ NMAD = 0.056 and 19.79% outliers. AGNBoost frac AGN estimates broadly agree with CIGALE fitting ( σ RMSE = 0.178, 11.96% outliers). The flexible framework allows straightforward incorporation of additional photometric bands and retraining for other variables. AGNBoost ’s computational efficiency makes it well suited for wide-sky surveys requiring rapid AGN identification and redshift estimation.
Source: The Astrophysical Journal
Authors: Kurt Hamblin, Allison Kirkpatrick, Bren E. Backhaus, Gregory Troiani, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne, Guang Yang, Micaela B. Bagley, Mark Dickinson, Steven L. Finkelstein, Pablo Arrabal Haro, Fabio Pacucci, Jonathan R. Trump, Nor Pirzkal, Alexander de la Vega, Edgar Perez Vidal, L. Y. Aaron Yung
Institutions: University of Kansas, Rochester Institute of Technology, Colby College, Space Telescope Science Institute, Goddard Space Flight Center, Texas A&M University, Chinese Academy of Sciences, Nanjing Institute of Astronomical Optics & Technology, The University of Texas at Austin, NSF NOIRLab, Harvard University, Center for Astrophysics Harvard & Smithsonian, University of Connecticut, University of California, Riverside, Tufts University
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W7160658354