The GOGREEN survey: transition galaxies and the evolution of environmental quenching
Bibliographic record
Abstract
ABSTRACT We measure the rate of environmentally driven star formation quenching in galaxies at z ∼ 1, using eleven massive ($M\approx 2\times 10^{14}\, \mathrm{M}_\odot$) galaxy clusters spanning a redshift range 1.0 < z < 1.4 from the GOGREEN sample. We identify three different types of transition galaxies: ‘green valley’ (GV) galaxies identified from their rest-frame (NUV − V) and (V − J) colours; ‘blue quiescent’ (BQ) galaxies, found at the blue end of the quiescent sequence in (U − V) and (V − J) colour; and spectroscopic post-starburst (PSB) galaxies. We measure the abundance of these galaxies as a function of stellar mass and environment. For high-stellar mass galaxies (log M/M⊙ > 10.5) we do not find any significant excess of transition galaxies in clusters, relative to a comparison field sample at the same redshift. It is likely that such galaxies were quenched prior to their accretion in the cluster, in group, filament, or protocluster environments. For lower stellar mass galaxies (9.5 < log M/M⊙ < 10.5) there is a small but significant excess of transition galaxies in clusters, accounting for an additional ∼5–10 per cent of the population compared with the field. We show that our data are consistent with a scenario in which 20–30 per cent of low-mass, star-forming galaxies in clusters are environmentally quenched every Gyr, and that this rate slowly declines from z = 1 to z = 0. While environmental quenching of these galaxies may include a long delay time during which star formation declines slowly, in most cases this must end with a rapid (τ < 1 Gyr) decline in star formation rate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".