SUPERDENSE MASSIVE GALAXIES IN THE ESO DISTANT CLUSTER SURVEY (EDisCS)
Bibliographic record
Abstract
We find a significant number of massive and compact galaxies in clusters from the ESO Distant Clusters Survey (EDisCS) at 0.4 < z < 1. They have similar stellar masses, ages, sizes, and axial ratios to local z ∼ 0.04 compact galaxies in WIde field Nearby Galaxy clusters Survey (WINGS) clusters, and to z = 1.4–2 massive and passive galaxies found in the general field. If non-brightest cluster galaxies of all densities, morphologies, and spectral types are considered, the median size of EDisCS galaxies is only a factor 1.18 smaller than in WINGS. We show that for morphologically selected samples, the morphological evolution taking place in a significant fraction of galaxies during the last Gyr may introduce an apparent, spurious evolution of size with redshift, which is actually due to intrinsic differences in the selected samples. We conclude that the median mass–size relation of cluster galaxies does not evolve significantly from z ∼ 0.7 to z ∼ 0.04. In contrast, the masses and sizes of BCGs and galaxies with M * >4 × 10 11 M ☉ have significantly increased by a factor of 2 and 4, respectively, confirming the results of a number of recent works on the subject. Our findings show that progenitor bias effects play an important role in the size-growth paradigm of massive and passive galaxies.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".