THE CARNEGIE-IRVINE GALAXY SURVEY. I. OVERVIEW AND ATLAS OF OPTICAL IMAGES
Bibliographic record
Abstract
The Carnegie-Irvine Galaxy Survey (CGS) is a long-term program to investigate the photometric and spectroscopic properties of a statistically complete sample of 605 bright ( B T < 12.9 mag), southern (δ < 0°) galaxies using the facilities at Las Campanas Observatory. This paper, the first in a series, outlines the scientific motivation of CGS, defines the sample, and describes the technical aspects of the optical broadband ( BVRI ) imaging component of the survey, including details of the observing program, data reduction procedures, and calibration strategy. The overall quality of the images is quite high, in terms of resolution (median seeing ∼1''), field of view (8 9 × 8 9), and depth (median limiting surface brightness ∼27.5, 26.9, 26.4, and 25.3 mag arcsec −2 in the B , V , R , and I bands, respectively). We prepare a digital image atlas showing several different renditions of the data, including three-color composites, star-cleaned images, stacked images to enhance faint features, structure maps to highlight small-scale features, and color index maps suitable for studying the spatial variation of stellar content and dust. In anticipation of upcoming science analyses, we tabulate an extensive set of global properties for the galaxy sample. These include optical isophotal and photometric parameters derived from CGS itself, as well as published information on multiwavelength (ultraviolet, U -band, near-infrared, far-infrared) photometry, internal kinematics (central stellar velocity dispersions, disk rotational velocities), environment (distance to nearest neighbor, tidal parameter, group, or cluster membership), and H i content. The digital images and science-level data products will be made publicly accessible to the community.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".