New Framework Combines Multiple Methods to Better Separate Active Galactic Nuclei from Host Galaxy Light
Astronomers have developed a unified framework that combines spectral energy distribution (SED) decomposition with deep-learning-based imaging to more reliably separate active galactic nucleus (AGN) emission from host-galaxy light. The study applies this approach to galaxies in the COSMOS-Web field using multi-wavelength photometry spanning ultraviolet to far-infrared, cross-checking results with JWST/NIRCam imaging. Accurately isolating AGN contributions is critical for correctly measuring host-galaxy physical properties and identifying AGN-dominated systems.
A new study accepted by Astronomy & Astrophysics presents a unified framework for estimating how much light in a galaxy comes from its central active galactic nucleus (AGN) versus the surrounding host galaxy. The researchers combined two independent SED-fitting codes — CIGALE and GRAHSP — with a deep-learning-based morphological decomposition applied to JWST/NIRCam F150W imaging of galaxies in the COSMOS-Web field. By comparing AGN contribution fractions derived from SED fitting against those from the imaging decomposition, the team identified significant parameter degeneracies inherent in current SED-fitting approaches that rely on empirical or theoretical AGN templates. The study demonstrates that incorporating independent morphological information can break these degeneracies, improving the reliability of both AGN fraction estimates and derived host-galaxy properties such as stellar mass and star formation rate. The work highlights a path toward more self-consistent multi-wavelength analyses of AGN host galaxies in deep survey fields.
What different sources said
- arXiv astro-phCenter
A Meta-Learning Framework for Multitask Reverberation Mapping in Active Galactic Nuclei
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