The FSVS Cluster Catalogue: galaxy clusters and groups in the Faint Sky Variability Survey
Bibliographic record
Abstract
We describe a large sample of 598 galaxy clusters and rich groups discovered in the data of the Faint Sky Variability Survey. The clusters have been identified using a fully automated, semi-parametric technique based on a maximum likelihood approach applied to Voronoi tessellation, and enhanced by colour discrimination. The sample covers a wide range of richness, has a density of ~28 clusters per sqdeg, and spans a range of estimated redshifts of 0.05 < z < 0.9 with mean = 0.345. Assuming the presence of a cluster red sequence, the uncertainty of the estimated cluster redshifts is assessed to be \~0.03. Containing over 100 clusters with z > 0.6, the catalogue contributes substantially to the current total of optically-selected, intermediate-redshift clusters, and complements the existing, usually X-ray selected, samples. The FSVS fields are accessible for observation throughout the whole year, making them particularly suited for large follow-up programmes. The construction of this FSVS Cluster Catalogue completes a fundamental component of our continuing programmes to investigate the environments of quasars and the chemical evolution of galaxies. We publish here the list of all clusters with their basic parameters, and discuss some illustrative examples in more detail. The full FSVS Cluster Catalogue, together with images and lists of member galaxies etc., will be issued as part of the ``NOAO data products'', and accessible at http://www.noao.edu/dpp/. We describe the format of these data and access to them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.024 |
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".