Insights into formation scenarios of massive early-type galaxies from spatially resolved stellar population analysis in CALIFA
Bibliographic record
Abstract
ABSTRACT We perform spatially resolved stellar population analysis for a sample of 69 early-type galaxies (ETGs) from the CALIFA integral field spectroscopic survey, including 48 ellipticals and 21 S0’s. We generate and quantitatively characterize profiles of light-weighted mean stellar age and metallicity within ≲2Re, as a function of radius and stellar-mass surface density μ*. We study in detail the dependence of profiles on galaxies’ global properties, including velocity dispersion σe, stellar mass, morphology. ETGs are universally characterized by strong, negative metallicity gradients ($\sim \!-0.3\, \text{dex}$ per Re) within 1Re, which flatten out moving towards larger radii. A quasi-universal local μ*–metallicity relation emerges, which displays a residual systematic dependence on σe, whereby higher σe implies higher metallicity at fixed μ*. Age profiles are typically U-shaped, with minimum around 0.4 Re, asymptotic increase to maximum ages beyond $\sim 1.5\, $Re, and an increase towards the centre. The depth of the minimum and the central increase anticorrelate with σe. A possible qualitative interpretation of these observations is a two-phase scenario. In the first phase, dissipative collapse occurs in the inner 1Re, establishing a negative metallicity gradient. The competition between the outside-in quenching due to feedback-driven winds and some form of inside-out quenching, possibly caused by central AGN feedback or dynamical heating, determines the U-shaped age profiles. In the second phase, the accretion of ex-situ stars from quenched and low-metallicity satellites shapes the flatter stellar population profiles in the outer regions.
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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) | 0.001 | 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".