The LEGA-C and SAMI Galaxy Surveys: Quiescent Stellar Populations and the Mass-Size Plane across 6 Gyr
Bibliographic record
Abstract
We investigate the change in mean stellar population age and metallicity ([Z/H]) scaling relations for quiescent galaxies from intermediate redshift ($0.60\leq z\leq0.76$) using the LEGA-C Survey, to low redshift ($0.014\leq z\leq0.10$) using the SAMI Galaxy Survey. We find that, similarly to their low-redshift counterparts, the stellar metallicity of quiescent galaxies at $0.60\leq z\leq 0.76$ closely correlates with $M_*/R_\mathrm{e}$ (a proxy for the gravitational potential or escape velocity), in that galaxies with deeper potential wells are more metal-rich. This supports the hypothesis that the relation arises due to the gravitational potential regulating the retention of metals, by determining the escape velocity required by metal-rich stellar and supernova ejecta to escape the system and avoid being recycled into later stellar generations. On the other hand, we find no correlation between stellar age and $M_*/R_\mathrm{e}^2$ (stellar mass surface density $\Sigma$) in the LEGA-C sample, despite this being a strong relation at low redshift. We consider this change in the age--$\Sigma$ relation in the context of the redshift evolution of the star-forming and quiescent populations in the mass--size plane, and find our results can be explained as a consequence of galaxies forming more compactly at higher redshifts, and remaining compact throughout their evolution. Furthermore, galaxies appear to quench at a characteristic surface density that decreases with decreasing redshift. The $z\sim 0$ age--$\Sigma$ relation is therefore a result of building up the quiescent and star-forming populations with galaxies that formed at a range of redshifts and so a range of surface densities.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".