Cloud-scale Molecular Gas Properties in 15 Nearby Galaxies
Bibliographic record
Abstract
Abstract We measure the velocity dispersion, σ , and surface density, Σ, of the molecular gas in nearby galaxies from CO spectral line cubes with spatial resolution 45–120 pc, matched to the size of individual giant molecular clouds. Combining 11 galaxies from the PHANGS-ALMA survey with four targets from the literature, we characterize ∼30,000 independent sightlines where CO is detected at good significance. Σ and σ show a strong positive correlation, with the best-fit power-law slope close to the expected value for resolved, self-gravitating clouds. This indicates only a weak variation in the virial parameter α vir ∝ σ 2 /Σ, which is ∼1.5–3.0 for most galaxies. We do, however, observe enormous variation in the internal turbulent pressure P turb ∝ Σ σ 2 , which spans ∼5 dex across our sample. We find Σ, σ , and P turb to be systematically larger in more massive galaxies. The same quantities appear enhanced in the central kiloparsec of strongly barred galaxies relative to their disks. Based on sensitive maps of M31 and M33, the slope of the σ –Σ relation flattens at Σ ≲ 10 M ⊙ pc −2 , leading to high σ for a given Σ and high apparent α vir . This echoes results found in the Milky Way and likely originates from a combination of lower beam-filling factors and a stronger influence of local environment on the dynamical state of molecular gas in the low-density regime.
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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".