WINGS: a WIde-field nearby Galaxy-cluster survey
Bibliographic record
Abstract
Context. This is the third paper in a series devoted to the WIde-field Nearby Galaxy-cluster Survey (WINGS). WINGS is a long-term project aimed at gathering wide-field, multiband imaging and spectroscopy of galaxies in a complete sample of 77 X-ray selected, nearby clusters () located far from the galactic plane (). The main goal of this project is to establish a local reference sample for evolutionary studies of galaxies and galaxy clusters.Aims. This paper presents the near-infrared () photometric catalogs of 28 clusters of the WINGS sample and describes the procedures followed to construct them.Methods. The raw data has been reduced at CASU and special care has been devoted to the final coadding, drizzling technique, astrometric solution, and magnitude calibration for the WFCAM pipeline-processed data. We constructed the photometric catalogs based on the final calibrated, coadded mosaics (≈0.79 deg2) in J (19 clusters) and K (27 clusters) bands. A customized interactive pipeline was used to clean the catalogs and to make mock images for photometric errors and completeness estimates.Results. We provide deep near-infrared photometric catalogs (90% complete in detection rate at total magnitudes , , and in classification rate at and ), giving positions, geometrical parameters, total and aperture magnitudes for all detected sources. For each field we classify the detected sources as stars, galaxies, and objects of “unknown” nature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".