Age and metal gradients in massive quiescent galaxies at 0.6 ≲ <i>z</i> ≲ 1.0: implications for quenching and assembly histories
Bibliographic record
Abstract
ABSTRACT We present spatially resolved, simple stellar population equivalent ages, stellar metallicities, and abundance ratios for 456 massive ($10.3\lesssim \log (\mathrm{M}_*/\mathrm{M}_\odot)\lesssim 11.8$) quiescent galaxies at $0.6\lesssim z\lesssim 1.0$ from the Large Early Galaxy Astrophysics Census, derived using full-spectrum models. Typically, we find flat age and [Mg/Fe] gradients, and negative [Fe/H] gradients, implying iron-rich cores. We also estimate intrinsic [Fe/H] gradients via forward modelling. We examine the observed gradients in three age bins. Younger quiescent galaxies typically have negative [Fe/H] gradients and positive age gradients, possibly indicating a recent central starburst. Additionally, this finding suggests that photometrically measured flat colour gradients in young quiescent galaxies are the result of the positive age and negative metallicity gradients cancelling each other. For older quiescent galaxies, the age gradients become flat and [Fe/H] gradients weaken, though remain negative. Thus, negative colour gradients at older ages are likely driven by metallicity gradients. The diminishing age gradient may result from the starburst fading. Furthermore, the persistence of the [Fe/H] gradients may suggest that the outskirts are simultaneously built up by mergers with lower metallicity satellites. On the other hand, the gradients could be inherited from the star-forming phase, in which case mergers may not be needed to explain our findings. This work illustrates the need for resolved spectroscopy, instead of just photometry, to measure stellar population gradients. Extending these measurements to higher redshift is imperative for understanding how stellar populations in quiescent galaxies are assembled over cosmic time.
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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 | 0.000 |
| 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".