The ALMaQUEST Survey. XIII. Understanding Radial Trends in Star Formation Quenching via the Relative Roles of Gas Availability and Star Formation Efficiency
Bibliographic record
Abstract
Abstract Star formation quenching is one of the key processes that shape the evolution of galaxies. In this study, we investigate the changes in molecular gas and star formation properties as galaxies transit from the star-forming main sequence to the passive regime. Our analysis reveals that as galaxies move away from the main sequence toward the green valley the radial profile of specific star formation rate surface density (Σ sSFR ) is suppressed compared with main-sequence galaxies out to a galactocentric radius of 1.5 R e (∼7 kpc for our sample). By combining radial profiles of gas fraction ( f gas ) and star formation efficiency (SFE), we can discern the underlying mechanism that determines Σ sSFR at different galactocentric radii. Analysis of relative contributions of f gas and SFE to Σ sSFR uncovers a diverse range of quenching modes. Star formation in approximately half of our quenching galaxies is primarily driven by a single mode (i.e., either f gas or SFE), or a combination of both. A collective analysis of all galaxies reveals that the reduction in star formation within the central regions ( R < 0.5 R e ) is primarily attributable to a decrease in SFE. Conversely, in the disk regions ( R > 0.5 R e ), both f gas and SFE contribute to the suppression of star formation. Our findings suggest that multiple quenching mechanisms may be at play in our sample galaxies, and even within a single galaxy. We also compare our observational outcomes with those from galaxy simulations and discuss the implications of our data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".