Keck Adaptive Optics Imaging of 0.5 < <i>z</i> < 1 Field Galaxies from the <i>Hubble Space Telescope</i> Archive
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 Å break at these redshifts and provide stellar fluxes less contaminated by dust. We fitted two-dimensional galaxy bulge+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 multicolor data set 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 signal-to-noise ratio 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.020 | 0.004 |
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".