AEGIS: THE MORPHOLOGIES OF GREEN GALAXIES AT 0.4 <<i>z</i>< 1.2
Bibliographic record
Abstract
We present quantitative morphologies of ∼300 galaxies in the optically defined green valley at 0.4 < z < 1.2, in order to constrain the mechanism(s) responsible for quenching star formation in the bulk of this population. The sample is selected from galaxies in the All-Wavelength Extended Groth Strip International Survey (AEGIS). While the green valley is defined using optical U − B colors, we find that using a green valley sample defined using NUV − R colors does not change the results. Using Hubble Space Telescope /Advanced Camera for Surveys imaging, we study several quantitative morphological parameters including CAS , B / T from GIM2D, and Gini/ M 20 . We find that the green galaxy population is intermediate between the red and blue galaxy populations in terms of concentration, asymmetry, and morphological type and merger fraction estimated using Gini/ M 20 . We find that most green galaxies are not classified as mergers; in fact, the merger fraction in the green valley is lower than in the blue cloud. We show that at a given stellar mass, green galaxies have higher concentration values than blue galaxies and lower concentration values than red galaxies. Additionally, we find that 12% of green galaxies have B / T = 0 and 21% have B / T ⩽ 0.05. Our results show that green galaxies are generally massive ( M * ∼ 10 10.5 M ☉ ) disk galaxies with high concentrations. We conclude that major mergers are likely not the sole mechanism responsible for quenching star formation in this population and that either other external processes or internal secular processes play an important role both in driving gas toward the center of these galaxies and in quenching star formation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".