The DEEP Groth Strip Galaxy Redshift Survey. III. Redshift Catalog and Properties of Galaxies
Bibliographic record
Abstract
The Deep Extragalactic Evolutionary Probe (DEEP) is a series of spectroscopic surveys of faint galaxies, targeted at the properties and clustering of galaxies at redshifts z ~ 1. We present the redshift catalog of the DEEP 1 GSS pilot phase of this project, a Keck/LRIS survey in the HST/WFPC2 Groth Survey Strip. The redshift catalog and data, including reduced spectra, are publicly available through a Web-accessible database. The catalog contains 658 secure galaxy redshifts with a median z=0.65, and shows large-scale structure walls to z = 1. We find a bimodal distribution in the galaxy color-magnitude diagram which persists to z = 1. A similar color division has been seen locally by the SDSS and to z ~ 1 by COMBO-17. For red galaxies, we find a reddening of only 0.11 mag from z ~ 0.8 to now, about half the color evolution measured by COMBO-17. We measure structural properties of the galaxies from the HST imaging, and find that the color division corresponds generally to a structural division. Most red galaxies, ~ 75%, are centrally concentrated, with a red bulge or spheroid, while blue galaxies usually have exponential profiles. However, there are two subclasses of red galaxies that are not bulge-dominated: edge-on disks and a second category which we term diffuse red galaxies (DIFRGs). The distant edge-on disks are similar in appearance and frequency to those at low redshift, but analogs of DIFRGs are rare among local red galaxies. DIFRGs have significant emission lines, indicating that they are reddened mainly by dust rather than age. The DIFRGs in our sample are all at z>0.64, suggesting that DIFRGs are more prevalent at high redshifts; they may be related to the dusty or irregular extremely red objects (EROs) beyond z>1.2 that have been found in deep K-selected surveys. (abridged)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".