A Model for the Onset of Self-gravitation and Star Formation in Molecular Gas Governed by Galactic Forces. I. Cloud-scale Gas Motions
Bibliographic record
Abstract
Abstract Modern extragalactic molecular gas surveys now reach the scales of star-forming giant molecular clouds (GMCs; 20–50 pc). Systematic variations in GMC properties with galaxy environment imply that clouds are not universally self-gravitating objects, decoupled from their surroundings. Here we re-examine the coupling of clouds to their environment and develop a model for 3D gas motions generated by forces arising with the galaxy gravitational potential defined by the background disk of stars and dark matter. We show that these motions can resemble or even exceed the motions needed to support gas against its own self-gravity throughout typical galactic disks. The importance of the galactic potential in spiral arms and galactic centers suggests that the response to self-gravity does not always dominate the motions of gas at GMC scales, with implications for observed gas kinematics, virial equilibrium, and cloud morphology. We describe how a uniform treatment of gas motions in the plane and in the vertical direction synthesizes the two main mechanisms proposed to regulate star formation: vertical pressure equilibrium and shear/Coriolis forces as parameterized by ToomreQ ≈ 1. As the modeled motions are coherent and continually driven by the external potential, they represent support for the gas that is distinct from that conventionally attributed to turbulence, which decays rapidly and thus requires maintenance, e.g., via feedback from star formation. Thus, our model suggests that the galaxy itself can impose an important limit on star formation, as we explore in a second paper in this series.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".