Keck Deep Fields. I. Observations, Reductions, and the Selection of Faint Star‐forming Galaxies at Redshifts<i>z</i>∼ 4, 3, and 2
Bibliographic record
Abstract
We introduce a very deep, lim ~ 27, multicolor imaging survey of very faint star-forming galaxies at z ~ 4, 3, 2.2, and 1.7. This survey, carried out on the Keck I telescope, uses the very same U n G I filter system that is employed by the Steidel team to select galaxies at these redshifts and thus allows us to construct identically selected but much fainter samples. However, our survey reaches ~1.5 mag deeper than the work of Steidel and his group, letting us probe substantially below the characteristic luminosity L * and thus study the properties and redshift evolution of the faint component of the high- z galaxy population. The survey covers 169 arcmin 2 in three spatially independent patches on the sky and—to ≤ 27—contains 427 G I -selected z ~ 4 Lyman break galaxies, 1481 U n G -selected z ~ 3 Lyman break galaxies, 2417 U n G -selected z ~ 2.2 star-forming galaxies, and 2043 U n G -selected z ~ 1.7 star-forming galaxies. In this paper, the first in a series, we introduce the survey, describe our observing and data reduction strategies, and outline the selection of our z ~ 4, 3, 2.2, and 1.7 samples.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.005 | 0.003 |
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".