THE NEXT GENERATION VIRGO CLUSTER SURVEY (NGVS). I. INTRODUCTION TO THE SURVEY*
Bibliographic record
Abstract
ABSTRACT The Next Generation Virgo Cluster Survey (NGVS) is a program that uses the 1 deg 2 MegaCam instrument on the Canada–France–Hawaii Telescope to carry out a comprehensive optical imaging survey of the Virgo cluster, from its core to its virial radius—covering a total area of 104 deg 2 —in the u * griz bandpasses. Thanks to a dedicated data acquisition strategy and processing pipeline, the NGVS reaches a point-source depth of g ≈ 25.9 mag (10σ) and a surface brightness limit of μ g ∼ 29 mag arcsec −2 (2σ above the mean sky level), thus superseding all previous optical studies of this benchmark galaxy cluster. In this paper, we give an overview of the technical aspects of the survey, such as areal coverage, field placement, choice of filters, limiting magnitudes, observing strategies, data processing and calibration pipelines, survey timeline, and data products. We also describe the primary scientific topics of the NGVS, which include: the galaxy luminosity and mass functions; the color–magnitude relation; galaxy scaling relations; compact stellar systems; galactic nuclei; the extragalactic distance scale; the large-scale environment of the cluster and its relationship to the Local Supercluster; diffuse light and the intracluster medium; galaxy interactions and evolutionary processes; and extragalactic star clusters. In addition, we describe a number of ancillary programs dealing with “foreground” and “background” science topics, including the study of high-inclination trans-Neptunian objects; the structure of the Galactic halo in the direction of the Virgo Overdensity and Sagittarius Stream; the measurement of cosmic shear, galaxy–galaxy, and cluster lensing; and the identification of distant galaxy clusters, and strong-lensing events.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".