A dynamical view on stellar metallicity gradient diversity across the Hubble sequence with CALIFA
Bibliographic record
Abstract
We analyse radial stellar metallicity and kinematic profiles out to 1Re in 244 CALIFA galaxies ranging from morphological type E to Sd, to study the evolutionary mechanisms of stellar population gradients. We find that linear metallicity gradients exhibit a clear correlation with galaxy morphological type – with early-type galaxies showing the steepest gradients. We show that the metallicity gradients simply reflect the local mass–metallicity relation within a galaxy. This suggests that the radial stellar population distribution within a galaxy’s effective radius is primarily a result of the in situ local star formation history. In this simple picture, the dynamically derived stellar surface mass density gradient directly predicts the metallicity gradient of a galaxy. We show that this correlation and its scatter can be reproduced entirely by using independent empirical galaxy structural and chemical scaling relations. Using Schwarzschild dynamical models, we also explore the link between a galaxy’s local stellar populations and their orbital structures. We find that galaxies’ angular momentum and metallicity gradients show no obvious causal link. This suggests that metallicity gradients in the inner disc are not strongly shaped by radial migration, which is confirmed by the lack of correlation between the metallicity gradients and observable probes of radial migration in the galaxies, such as bars and spiral arms. Finally, we find that galaxies with positive metallicity gradients become increasingly common towards low mass and late morphological types – consistent with stellar feedback more efficiently modifying the baryon cycle in the central regions of these galaxies.
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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".