Papers › AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI...
AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry
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
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We present AGNBoost, a machine learning framework utilizing XGBoostLSS to identify AGN and estimate redshifts from JWST NIRCam and MIRI photometry. AGNBoost constructs 66 input features from 7 NIRCam and 4 MIRI bands to predict the fraction of mid-IR $3$--$30\,\mu$m emission attributable to an AGN power law (frac_(AGN)) and photometric redshift. Each model is trained on 10⁶ simulated galaxies from 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 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. On 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 re-training for other variables. AGNBoost's computational efficiency makes it well-suited for wide-sky surveys requiring rapid AGN identification and redshift estimation.
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