The EDGE-CALIFA Survey: Molecular Gas and Star Formation Activity across the Green Valley
Bibliographic record
Abstract
Abstract We present a 12 CO( J = 2−1) survey of 60 local galaxies using data from the Atacama Compact Array as part of the Extragalactic Database for Galaxy Evolution: the ACA EDGE survey. These galaxies all have integral field spectroscopy from the CALIFA survey. Compared to other local galaxy surveys, ACA EDGE is designed to mitigate selection effects based on CO brightness and morphological type. Of the 60 galaxies in ACA EDGE, 36 are on the star formation main sequence, 13 are on the red sequence, and 11 lie in the “green valley” transition between these sequences. We test how star formation quenching processes affect the star formation rate (SFR) per unit molecular gas mass, SFE mol = SFR/ M mol , and related quantities in galaxies with stellar masses 10 ≤ log[ M ⋆ / M ⊙ ] ≤ 11.5 covering the full range of morphological types. We observe a systematic decrease of the molecular-to-stellar mass fraction ( R ⋆ mol ) with a decreasing level of star formation activity, with green valley galaxies also having lower SFE mol than galaxies on the main sequence. On average, we find that the spatially resolved SFE mol within the bulge region of green valley galaxies is lower than in the bulges of main-sequence galaxies if we adopt a constant CO-to-H 2 conversion factor, α CO . While efficiencies in main-sequence galaxies remain almost constant with galactocentric radius, in green valley galaxies, we note a systematic increase of SFE mol , R ⋆ mol , and specific SFR with increasing radius. As shown in previous studies, our results suggest that although gas depletion (or removal) seems to be the most important driver of the star formation quenching in galaxies transiting through the green valley, a reduction in star formation efficiency is also required during this stage.
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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 | 0.001 | 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) | 0.002 | 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".