Ages and metallicities for quiescent galaxies in the Shapley supercluster: driving parameters of the stellar populations
Bibliographic record
Abstract
We use high signal-to-noise spectroscopy for a sample of 232 quiescent galaxies in the Shapley supercluster, to investigate how their stellar populations depend on velocity dispersion (σ), luminosity and stellar mass. The sample spans a large range in velocity dispersion (30–300 km s−1) and in luminosity (MR from −18.7 to −23.2). Estimates of age, total metallicity (Z/H) and α-element abundance ratio (α/Fe) were derived from absorption-line analysis, using single-burst models of Thomas and collaborators. Using the Rose Ca ii index, we conclude that recent star formation (frosting) events are not responsible for the intermediate ages observed in some of the galaxies. Age, Z/H and α/Fe are correlated positively with velocity dispersion, but we also find significant residual trends with luminosity: at given σ, the brighter galaxies are younger, less α-enriched and have higher Z/H. At face value, these results might suggest that the stellar populations depend on stellar mass as well as on velocity dispersion. However, we show that the observed trends can be reproduced by models in which the stellar populations depend systematically only onσ, and are independent of stellar mass M*. For age, the observed luminosity correlation arises because young galaxies are brighter, at fixed M*. For metallicity, the observed luminosity dependence arises because metal-rich galaxies, at fixed mass, tend also to be younger, and hence brighter. We find a good match to the observed luminosity correlations with age ∝ σ+0.40, Z/H ∝ σ+0.35, α/Fe ∝ σ+0.20, where the slopes are close to those found when fitting traditional scaling relations. We conclude that the star formation and enrichment histories of galaxies are determined primarily by the depth of their gravitational potential wells. The observed residual correlations with luminosity do not imply a corresponding dependence on stellar mass.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".