EARLY-TYPE GALAXIES AT<i>z</i>∼ 1.3. IV. SCALING RELATIONS IN DIFFERENT ENVIRONMENTS
Bibliographic record
Abstract
We present the Kormendy and mass–size relations (MSR) for early-type galaxies (ETGs) as a function of environment at z ∼ 1.3. Our sample includes 76 visually classified ETGs with masses 10 10 < M / M ☉ < 10 11.5 , selected in the Lynx supercluster and in the Great Observatories Origins Deep Survey/Chandra Deep Field South field; 31 ETGs in clusters, 18 in groups, and 27 in the field, all with multi-wavelength photometry and Hubble Space Telescope /Advanced Camera for Surveys observations. The Kormendy relation, in place at z ∼ 1.3, does not depend on the environment. The MSR reveals that ETGs overall appear to be more compact in denser environments: cluster ETGs have sizes on average around 30%–50% smaller than those of the local universe and a distribution with a smaller scatter, whereas field ETGs show an MSR with a similar distribution to the local one. Our results imply that (1) the MSR in the field did not evolve overall from z ∼ 1.3 to present; this is interesting and in contrast to the trend found at higher masses from previous works; (2) in denser environments, either ETGs have increased in size by 30%–50% on average and spread their distributions, or more ETGs have been formed within the dense environment from non-ETG progenitors, or larger galaxies have been accreted to a pristine compact population to reproduce the MSR observed in the local universe. Our results are driven by galaxies with masses M ≲ 2 × 10 11 M ☉ and those with masses M ∼ 10 11 M ☉ follow the same trends as that of the entire sample. Following the Valentinuzzi et al. definition of superdense ETGs, ∼35%–45% of our cluster sample is made up of superdense ETGs.
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 | 0.000 | 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 | 0.001 |
| 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".