GALFIT-ing AGN Host Galaxies in COSMOS: HST versus Subaru
Bibliographic record
Abstract
Abstract The COSMOS field has been extensively observed by most major telescopes, including Chandra, HST, and Subaru. HST imaging boasts very high spatial resolution and is used extensively in morphological studies of distant galaxies. Subaru provides lower spatial resolution imaging than HST but a substantially wider field of view with greater sensitivity. Both telescopes provide near-infrared imaging of COSMOS. Successful morphological fitting of Subaru data would allow us to measure morphologies of over 104 known active galactic nucleus (AGN) hosts, accessible through Subaru wide-field surveys, currently not covered by HST. The morphological parameters indicate the types of galaxies that host AGNs. For 4016 AGNs between 0.03 < z < 6.5, we study the morphology of their galaxy hosts using GALFIT, fitting components representing the AGN and host galaxy simultaneously using the i-band imaging from both HST and Subaru. Comparing the fits for the differing telescope spatial resolutions and image signal-to-noise ratios, we identify parameter regimes for which there is strong disagreement between distributions of fitted parameters for HST and Subaru. In particular, the Sérsic index values strongly disagree between the two sets of data, including sources at lower redshifts. In contrast, the measured magnitude and radius parameters show reasonable agreement. Additionally, large variations in the Sérsic index have little effect on the χ ν 2 of each fit, whereas variations in other parameters have a more significant effect. These results indicate that the Sérsic index distributions of high-redshift galaxies that host AGNs imaged at ground-based spatial resolution are not reliable indicators of galaxy type and should be interpreted with caution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".