CLEAR: The Morphological Evolution of Galaxies in the Green Valley
Bibliographic record
Abstract
Abstract Quiescent galaxies having more compact morphologies than star-forming galaxies has been a consistent result in the field of galaxy evolution. What is not clear is at what point this divergence happens, i.e., when do quiescent galaxies become compact, and how big of a role does the progenitor effect play in this result? Here we aim to model the morphological and star formation histories of high-redshift (0.8 < z < 1.65) massive galaxies ( log ( M / M ⊙ ) > 10.2) with stellar population fits using Hubble Space Telescope/WFC3 G102 and G141 grism spectra plus photometry from the CANDELS Lyα Emission at Reionization (CLEAR) survey, constraining the star formation histories for a sample of ∼400 massive galaxies using flexible star formation histories. We develop a novel approach to classifying galaxies by their star formation activity in a way that highlights the green valley population, by modeling the specific star formation rate distributions as a function of redshift and deriving the probability that a galaxy is quiescent (P Q ). Using P Q and our flexible star formation histories we outline the evolutionary paths of our galaxies in relation to stellar mass, Sérsic index, effective radius R eff, and stellar mass surface density. We find that the galaxies show no appreciable stellar mass growth after entering the green valley (a net decrease of 4%) while their stellar mass surface densities increase by ∼0.25 dex. Therefore galaxies are becoming compact during the green valley and this is due to an increase in the Sérsic index and a decrease in R eff.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".