摘要
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.