The GOGREEN Survey: Evidence of an Excess of Quiescent Disks in Clusters at 1.0 < z < 1.4
Bibliographic record
Abstract
Abstract We present the results of the measured shapes of 832 galaxies in 11 galaxy clusters at 1.0 < z < 1.4 from the GOGREEN survey. We measure the axis ratio (q), the ratio of the minor to the major axis, of the cluster galaxies from near-infrared Hubble Space Telescope imaging using Sérsic profile fitting and compare them with a field sample. We find that the median q of both star-forming and quiescent galaxies in clusters increases with stellar mass, similar to the field. Comparing the axis ratio distributions between clusters and the field in four mass bins, the distributions for star-forming galaxies in clusters are consistent with those in the field. Conversely, the distributions for quiescent galaxies in the two environments are distinct, most remarkably in 10.1 ≤ log ( M / M ⊙ ) < 10.5 where clusters show a flatter distribution, with an excess at low q. Modelling the distribution with oblate and triaxial components, we find that the cluster and field sample difference is consistent with an excess of flattened oblate quiescent galaxies in clusters. The oblate population contribution drops at high masses, resulting in a narrower q distribution in the massive population than at lower masses. Using a simple accretion model, we show that the observed q distributions and quenched fractions are consistent with a scenario where no morphological transformation occurs for the environmentally quenched population in the two intermediate-mass bins. Our results suggest that environmental quenching mechanism(s) likely produce a population that has a different morphological mix than those resulting from the dominant quenching mechanism in the field.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".