The Organization of Cloud-scale Gas Density Structure: High-resolution CO versus 3.6 μm Brightness Contrasts in Nearby Galaxies
Bibliographic record
Abstract
Abstract In this paper we examine the factors that shape the distribution of molecular gas surface densities on the 150 pc scale across 67 morphologically diverse star-forming galaxies in the PHANGS-ALMA CO (2–1) survey. Dividing each galaxy into radial bins, we measure molecular gas surface density contrasts, defined here as the ratio between a fixed high percentile of the CO distribution and a fixed reference level in each bin. This reference level captures the level of the faint CO floor that extends between bright filamentary features, while the intensity level of the higher percentile probes the structures visually associated with bright, dense interstellar medium features like spiral arms, bars, and filaments. We compare these contrasts to matched percentile-based measurements of the 3.6 μm emission measured using Spitzer/IRAC imaging, which trace the underlying stellar mass density. We find that the logarithms of CO contrasts on 150 pc scales are 3–4 times larger than, and positively correlated with, the logarithms of 3.6 μm contrasts probing smooth nonaxisymmetric stellar bar and spiral structures. The correlation appears steeper than linear, consistent with the compression of gas as it flows supersonically in response to large-scale stellar structures, even in the presence of weak or flocculent spiral arms. Stellar dynamical features appear to play an important role in setting the cloud-scale gas density in our galaxies, with gas self-gravity perhaps playing a weaker role in setting the 150 pc scale distribution of gas densities.
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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 | 0.000 |
| 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".