Improved SED-fitting Assumptions Result in Inside-out Quenching at z ~ 0.5 and Quenching at All Radii Simultaneously at z ~ 1
Bibliographic record
Abstract
Abstract Many studies conclude that galaxies quench from the inside-out by examining profiles of specific star formation rate (sSFR). These are usually measured by fitting spectral energy distributions (SEDs) assuming a fixed dust law and uniform priors on all parameters. Here, we examine the effects of more physically motivated priors: a flexible dust law, an exponential prior on the dust attenuation A V, and Gaussian priors that favor extended star formation histories. This results in model colors that better trace observations. We then perform radial SED fits to multiband flux profiles measured from Hubble Space Telescope images for 1440 galaxies at 0.4 < z < 1.5 of stellar masses 1010–1011.5 M ⊙ using both the traditional and the more physically motivated assumptions. The latter results in star formation rate and A V profiles that agree with measurements from spectroscopy and A V profiles that behave correctly as a function of inclination. Since green valley galaxies at z ∼ 1.3 are expected to evolve into quiescent galaxies at z ∼ 0.9, we compare their sSFR profiles using the more physically motivated assumptions. Their slopes are similar at all masses (0.06–0.08 dex kpc−1), and the normalizations for the quiescent galaxies are lower. Therefore, the sSFR profiles decline with time as quenching occurs at all radii simultaneously. We compare profiles of green valley galaxies at z ∼ 0.9 and quiescent galaxies at z ∼ 0.5. The former are shallower at all masses by ~0.1 dex kpc−1. The sSFR profiles steepen with time as galaxies quench from the inside-out. In summary, galaxies at z ∼ 1 quench at all radii simultaneously while galaxies at z ∼ 0.7 quench from the inside-out.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".