Stellar Kinematics and Environment at z ∼ 0.8 in the LEGA-C Survey: Massive Slow Rotators Are Built First in Overdense Environments
Bibliographic record
Abstract
Abstract In this Letter, we investigate the impact of environment on integrated and spatially resolved stellar kinematics of a sample of massive, quiescent galaxies at intermediate redshift (0.6 < z < 1.0). For this analysis, we combine photometric and spectroscopic parameters from the UltraVISTA and Large Early Galaxy Astrophysics Census surveys in the COSMOS field and environmental measurements. We analyze the trends with overdensity (1+δ) on the rotational support of quiescent galaxies and find no universal trends at either fixed mass or fixed stellar velocity dispersion. This is consistent with previous studies of the local universe; rotational support of massive galaxies depends primarily on stellar mass. We highlight two populations of massive galaxies ( ) that deviate from the average mass relation. First, the most massive galaxies in the most underdense regions ((1 + δ) ≤ 1) exhibit elevated rotational support. Similarly, at the highest masses ( ) the range in rotational support is significant in all but the densest regions. This corresponds to an increasing slow-rotator fraction such that only galaxies in the densest environments ((1 + δ) ≥ 3.5) are primarily (90% ± 10%) slow rotators. This effect is not seen at fixed velocity dispersion, suggesting minor merging as the driving mechanism: only in the densest regions have the most massive galaxies experienced significant minor merging, building stellar mass and diminishing rotation without significantly affecting the central stellar velocity dispersion. In the local universe, most massive galaxies are slow rotators, regardless of environment, suggesting minor merging occurs at later cosmic times (z ≲ 0.6) in all but the most dense environments.
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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".