AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry
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| Format: | Preprint |
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2025
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| author | Hamblin, Kurt Kirkpatrick, Allison Backhaus, Bren E. Troiani, Gregory Kartaltepe, Jeyhan S. Kocevski, Dale D. Koekemoer, Anton M. Lambrides, Erini Papovich, Casey Ronayne, Kaila Yang, Guang Bagley, Micaela B. Dickinson, Mark Finkelstein, Steven L. Haro, Pablo Arrabal Pacucci, Fabio Trump, Jonathan R. Pirzkal, Nor de la Vega, Alexander Vidal, Edgar Perez Yung, L. Y. Aaron |
| author_facet | Hamblin, Kurt Kirkpatrick, Allison Backhaus, Bren E. Troiani, Gregory Kartaltepe, Jeyhan S. Kocevski, Dale D. Koekemoer, Anton M. Lambrides, Erini Papovich, Casey Ronayne, Kaila Yang, Guang Bagley, Micaela B. Dickinson, Mark Finkelstein, Steven L. Haro, Pablo Arrabal Pacucci, Fabio Trump, Jonathan R. Pirzkal, Nor de la Vega, Alexander Vidal, Edgar Perez Yung, L. Y. Aaron |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_03130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry Hamblin, Kurt Kirkpatrick, Allison Backhaus, Bren E. Troiani, Gregory Kartaltepe, Jeyhan S. Kocevski, Dale D. Koekemoer, Anton M. Lambrides, Erini Papovich, Casey Ronayne, Kaila Yang, Guang Bagley, Micaela B. Dickinson, Mark Finkelstein, Steven L. Haro, Pablo Arrabal Pacucci, Fabio Trump, Jonathan R. Pirzkal, Nor de la Vega, Alexander Vidal, Edgar Perez Yung, L. Y. Aaron Astrophysics of Galaxies 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. |
| title | AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2506.03130 |