Bibliographic record
Abstract
We have employed natural guide star adaptive optics (AO) on the Keck II telescope to obtain near-infrared (H and K') images of three field galaxies, each of redshift greater than 0.5. These are among the highest-redshift non-active disk galaxies to be imaged with AO. Each of the galaxies was chosen because it had been observed previously with the Hubble Space Telescope (HST) Wide Field Planetary Camera 2 (WFPC2) by others. Our AO images in the near infrared (NIR) closely match both the depth and high spatial resolution of those optical data. Combining the archival V and I data with our new H and K' images potentially produces a long wavelength baseline at uniform resolution. The NIR data probe emission well longward of the 4000-Angstrom break at these redshifts, and provide stellar fluxes less contaminated by dust. We fit two-dimensional galaxy bulge-plus-disk models simultaneously in all bands, and compare stellar-population-synthesis modeling to the photometry of these separated components. This is an initial foray into combining HST and AO imaging to produce a high spatial-resolution multi-color dataset for a large sample of faint galaxies. Our pilot program shows that NIR AO images from large ground-based observatories, augmented by HST images in the optical, can in principle provide a powerful tool for the analysis of faint field galaxies. However, the AO data S/N will have to be increased, and AO PSFs need to be controlled more carefully than they were here.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.023 | 0.006 |
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".