Centrally concentrated molecular gas driving galactic-scale ionized gas outflows in star-forming galaxies
Bibliographic record
Abstract
ABSTRACT We perform a joint analysis of high spatial resolution molecular gas and star-formation rate (SFR) maps in main-sequence star-forming galaxies experiencing galactic-scale outflows of ionized gas. Our aim is to understand the mechanism that determines which galaxies are able to launch these intense winds. We observed CO(1→0) at 1-arcsec resolution with ALMA in 16 edge-on galaxies, which also have 2-arcsec spatial-resolution optical integral field observations from the SAMI Galaxy Survey. Half the galaxies in the sample were previously identified as harbouring intense and large-scale outflows of ionized gas (‘outflow types’) and the rest serve as control galaxies. The data set is complemented by integrated CO(1→0) observations from the IRAM 30-m telescope to probe the total molecular gas reservoirs. We find that the galaxies powering outflows do not possess significantly different global gas fractions or star-formation efficiencies when compared with a control sample. However, the ALMA maps reveal that the molecular gas in the outflow-type galaxies is distributed more centrally than in the control galaxies. For our outflow-type objects, molecular gas and star-formation are largely confined within their inner effective radius (reff), whereas in the control sample, the distribution is more diffuse, extending far beyond reff. We infer that outflows in normal star-forming galaxies may be caused by dynamical mechanisms that drive molecular gas into their central regions, which can result in locally enhanced gas surface density and 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".