Colors, Magnitudes, and Velocity Dispersions in Early-Type Galaxies: Implications for Galaxy Ages and Metallicities
Bibliographic record
Abstract
We present an analysis of the color–magnitude–velocity dispersion relation for a sample of 39,320 early-type galaxies within the Sloan Digital Sky Survey. We demonstrate that the color-magnitude relation is entirely a consequence of the fact that both the luminosities and colors of these galaxies are correlated with stellar velocity dispersions. Previous studies of the color-magnitude relation over a range of redshifts suggest that the luminosity of an early-type galaxy is an indicator of its metallicity, whereas residuals in color from the relation are indicators of the luminosity-weighted age of its stars. We show that this, when combined with our finding that velocity dispersion plays a crucial role, has a number of interesting implications. First, galaxies with large velocity dispersions tend to be older (i.e., they scatter redward of the color-magnitude relation). Similarly, galaxies with large dynamical mass estimates also tend to be older. In addition, at fixed luminosity galaxies that are smaller or have larger velocity dispersions or are more massive tend to be older. Second, models in which galaxies with the largest velocity dispersions are also the most metal-poor are difficult to reconcile with our data. However, at fixed velocity dispersion galaxies have a range of ages and metallicities: the older galaxies have smaller metallicities and vice versa. Finally, a plot of velocity dispersion versus luminosity can be used as an age indicator: lines of constant age run parallel to the correlation between velocity dispersion and luminosity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".