The EDGE-CALIFA survey: exploring the role of molecular gas on galaxy star formation quenching
Bibliographic record
Abstract
Understanding how galaxies cease to form stars represents an outstanding challenge for galaxy evolution theories. This process of “star formation quenching” has been related to various causes, including active galactic nuclei activity, the influence of large-scale dynamics, and the environment in which galaxies live. In this paper, we present the first results from a follow-up of CALIFA survey galaxies with observations of molecular gas obtained with the APEX telescope. Together with the EDGE-CARMA observations, we collected 12 CO observations that cover approximately one effective radius in 472 CALIFA galaxies. We observe that the deficit of galaxy star formation with respect to the star formation main sequence (SFMS) increases with the absence of molecular gas and with a reduced efficiency of conversion of molecular gas into stars, which is in line with the results of other integrated studies. However, by dividing the sample into galaxies dominated by star formation and galaxies quenched in their centres (as indicated by the average value of the H α equivalent width), we find that this deficit increases sharply once a certain level of gas consumption is reached, indicating that different mechanisms drive separation from the SFMS in star-forming and quenched galaxies. Our results indicate that differences in the amount of molecular gas at a fixed stellar mass are the primary drivers for the dispersion in the SFMS, and the most likely explanation for the start of star formation quenching. However, once a galaxy is quenched, changes in star formation efficiency drive how much a retired galaxy differs in its star formation rate from star-forming ones of similar masses. In other words, once a paucity of molecular gas has significantly reduced star formation, changes in the star formation efficiency are what drives a galaxy deeper into the red cloud, hence retiring it.
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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 | 0.001 |
| 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) | 0.001 | 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".