THE NEXT GENERATION VIRGO CLUSTER SURVEY. XX. RedGOLD BACKGROUND GALAXY CLUSTER DETECTIONS
Bibliographic record
Abstract
ABSTRACT We build a background cluster candidate catalog from the Next Generation Virgo Cluster Survey (NGVS) using our detection algorithm RedGOLD. The NGVS covers 104 deg2 of the Virgo cluster in the -bandpasses to a depth of g ∼ 25.7 mag (5σ). Part of the survey was not covered or has shallow observations in the r band. We build two cluster catalogs: one using all bandpasses, for the fields with deep r-band observations (∼20 deg2), and the other using four bandpasses ( ) for the entire NGVS area. Based on our previous Canada–France–Hawaii Telescope Legacy Survey W1 studies, we estimate that both of our catalogs are ∼100% (∼70%) complete and ∼80% pure, at z ≤ 0.6 (z ≲ 1), for galaxy clusters with masses of M ≳ 1014 M ⊙. We show that when using four bandpasses, though the photometric redshift accuracy is lower, RedGOLD detects massive galaxy clusters up to z ∼ 1 with completeness and purity similar to the five-band case. This is achieved when taking into account the bias in the richness estimation, which is ∼40% lower at 0.5 ≤ z < 0.6 and ∼20% higher at 0.6 < z < 0.8, with respect to the five-band case. RedGOLD recovers all the X-ray clusters in the area with mass M 500 > 1.4 × 1014 M ⊙ and 0.08 < z < 0.5. Because of our different cluster richness limits and the NGVS depth, our catalogs reach lower masses than the published redMaPPer cluster catalog over the area, and we recover ∼90%–100% of its detections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".