AGNBoost: A Machine Learning Approach to Active Galactic Nuclei Identification with JWST/NIRCam+MIRI Colors and Photometry

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 seve...

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Hlavní autoři:	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
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IOP Publishing 2026-01-01
Edice:	The Astrophysical Journal
Témata:	Active galactic nuclei Infrared sources Regression AGN host galaxies
On-line přístup:	https://doi.org/10.3847/1538-4357/ae4d3f
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