AGN--Host Galaxy Image Decomposition with JWST
Bibliographic record
Abstract
The ability to disentangle the light of an AGN from its host galaxy is strongly dependent on the spatial resolution and depth of the imaging. As the capabilities of imaging systems improve with time, confirming that our standard techniques adequately model the increasingly complex structures unveiled is essential. With JWST providing unprecedented image quality, we can test how measurements of galaxy morphology vary with the choice of point-spread function (PSF) and fitting software. We perform two-component Sérsic+PSF fits of the surface brightness profiles of 87 X-ray AGNs $(0.1 < z < 4)$ from the CEERS survey. We create model PSFs for NIRCam F115W imaging using both photutils and PSFEx. We find that PSFEx models consistently fail to match the radial profile of typical point sources within our sample. We then perform AGN--host decompositions on each source by creating Sérsic+PSF models using both Galfit and AstroPhot. We find that Galfit and AstroPhot converge to different regions of the parameter space, providing consistently differing host galaxy properties. While we can measure the AGN and host magnitudes accurately, we find that the host galaxy morphological parameters are not well-determined -- the Sérsic index and effective radius are strongly covariant. Significant changes in the host galaxy parameters do not correspond to changes in the statistical quality of fit, nor to significant changes in the model's radial profile. These results indicate that the Sérsic profile does not uniquely well-represent typical AGN host galaxies in extragalactic survey fields. We also provide recommendations for studies of AGN hosts comparable to ours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".