The DEEP Groth Strip Survey. I. The Sample
Bibliographic record
Abstract
The Deep Extragalactic Exploratory Probe (DEEP) is a multi-phase research program dedicated to the study of the formation and evolution of galaxies and of large scale structure in the distant Universe. This paper describes the first five-year phase, denoted DEEP1. A series of ten DEEP1 papers will discuss a range of scientific topics (e.g., the study of photometric and spectral properties of a general distant galaxy survey, the evolution observed in galaxy populations of varied morphologies). The observational basis for these studies is the Groth Survey Strip field, a 127 square arcminute region which has been observed with the Hubble Space Telescope in both broad I-band and V-band optical filters and with the Low Resolution Imaging Spectrograph on the Keck Telescopes. Catalogs of photometric and structural parameters have been constructed for 11,547 galaxies and stars at magnitudes brighter than 29, and spectroscopy has been conducted for a magnitude-color weighted subsample of 818 objects. We evaluate three independent techniques for constructing an imaging catalog for the field from the HST data, and discuss the depth and sampling of the resultant catalogs. The selection of the spectroscopic subsample is discussed, and we describe the multifaceted approach taken to prioritizing objects of interest for a variety of scientific subprograms. A series of Monte Carlo simulations then demonstrates that the spectroscopic subsample can be adequately modeled as a simple function of magnitude and color cuts in the imaging catalog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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 | 0.003 |
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; both teacher heads agree on what is shown here.
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".