AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry
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 $3$--$30\,μ$m emission attributable to an AGN power law ($\text{frac}_{\text{AGN}}$) and photometric redshift. Each model is trained on $10^6$ 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 $1.63\%$ ($\text{frac}_{\text{AGN}}$) and $0.15\%$ (redshift), with $σ_{\text{RMSE}} = 0.045$ for $\text{frac}_{\text{AGN}}$ and $σ_{\text{NMAD}} = 0.004$ 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 $\text{frac}_{\text{AGN}} > 0.3$ and $100\%$ with $\text{frac}_{\text{AGN}} > 0.5$, demonstrating generalization beyond the training distribution. On MEGA galaxies with spectroscopic redshifts, AGNBoost achieves $σ_{\text{NMAD}} = 0.056$ and $19.79\%$ outliers. AGNBoost $\text{frac}_{\text{AGN}}$ estimates broadly agree with CIGALE fitting ($σ_{\text{RMSE}} = 0.178$, $11.96\%$ 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.