The LEGA-C of Nature and Nurture in Stellar Populations at z ∼ 0.6–1.0: D<sub> n </sub>4000 and Hδ Reveal Different Assembly Histories for Quiescent Galaxies in Different Environments
Bibliographic record
Abstract
Abstract Galaxy evolution is driven by a variety of physical processes that are predicted to proceed at different rates for different dark matter haloes and environments across cosmic times. A record of this evolution is preserved in galaxy stellar populations, which we can access using absorption-line spectroscopy. Here we explore the large LEGA-C survey (DR3) to investigate the role of the environment and stellar mass on stellar populations at z ∼ 0.6–1 in the COSMOS field. Leveraging the statistical power and depth of LEGA-C, we reveal significant gradients in D n 4000 and Hδ equivalent widths (EWs) distributions over the stellar mass versus environment 2D spaces for the massive galaxy population (M > 1010 M ⊙) at z ∼ 0.6–1.0. D n 4000 and Hδ EWs primarily depend on stellar mass, but they also depend on environment at fixed stellar mass. By splitting the sample into centrals and satellites, and in terms of star-forming galaxies and quiescent galaxies, we reveal that the significant environmental trends of D n 4000 and Hδ EW, when controlling for stellar mass, are driven by quiescent galaxies. Regardless of being centrals or satellites, star-forming galaxies reveal D n 4000 and Hδ EWs, which depend strongly on their stellar mass and are completely independent of the environment at 0.6 < z < 1.0. The environmental trends seen for satellite galaxies are fully driven by the trends that hold only for quiescent galaxies, combined with the strong environmental dependency of the quiescent fraction at fixed stellar mass. Our results are consistent with recent predictions from simulations that point toward massive galaxies forming first in overdensities or the most compact dark matter haloes.
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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) | 0.002 | 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".