THE RED-SEQUENCE CLUSTER SURVEY-2 (RCS-2): SURVEY DETAILS AND PHOTOMETRIC CATALOG CONSTRUCTION
Bibliographic record
Abstract
The second Red-sequence Cluster Survey (RCS-2) is a ∼1000 deg 2 , multi-color imaging survey using the square-degree imager, MegaCam, on the Canada–France–Hawaii Telescope. It is designed to detect clusters of galaxies over the redshift range 0.1 ≲ z ≲ 1. The primary aim is to build a statistically complete, large (∼10 4 ) sample of clusters, covering a sufficiently long redshift baseline to be able to place constraints on cosmological parameters via the evolution of the cluster mass function. Other main science goals include building a large sample of high surface brightness, strongly gravitationally lensed arcs associated with these clusters, and an unprecedented sample of several tens of thousands of galaxy clusters and groups, spanning a large range of halo mass, with which to study the properties and evolution of their member galaxies. This paper describes the design of the survey and the methodology for acquiring, reducing, and calibrating the data for the production of high-precision photometric catalogs. We describe the method for calibrating our griz imaging data using the colors of the stellar locus and overlapping Two Micron All Sky Survey photometry. This yields an absolute accuracy of <0.03 mag on any color and ≈0.05 mag in the r -band magnitude, verified with respect to the Sloan Digital Sky Survey (SDSS). Our astrometric calibration is accurate to ≪0 3 from comparison with SDSS positions. RCS-2 reaches average 5σ point-source limiting magnitudes of griz = [24.4, 24.3, 23.7, 22.8], approximately 1–2 mag deeper than the SDSS. Due to the queue-scheduled nature of the observations, the data are highly uniform and taken in excellent seeing, mostly FWHM ≲ 0 7 in the r band. In addition to the main science goals just described, these data form the basis for a number of other planned and ongoing projects (including the WiggleZ survey), making RCS-2 an important next-generation imaging survey.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.031 |
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".