A Model for the Onset of Self-gravitation and Star Formation in Molecular Gas Governed by Galactic Forces. II. The Bottleneck to Collapse Set by Cloud–Environment Decoupling
Bibliographic record
Abstract
Abstract In Meidt et al., we showed that gas kinematics on the scale of individual molecular clouds are not entirely dominated by self-gravity but also track a component that originates with orbital motion in the potential of the host galaxy. This agrees with observed cloud line widths, which show systematic variations from virial motions with environment, pointing at the influence of the galaxy potential. In this paper, we hypothesize that these motions act to slow down the collapse of gas and so help regulate star formation. Extending the results of Meidt et al., we derive a dynamical collapse timescale that approaches the free-fall time only once the gas has fully decoupled from the galactic potential. Using this timescale, we make predictions for how the fraction of free-falling, strongly self-gravitating gas varies throughout the disks of star-forming galaxies. We also use this collapse timescale to predict variations in the molecular gas star formation efficiency, which is lowered from a maximum, feedback-regulated level in the presence of strong coupling to the galactic potential. Our model implies that gas can only decouple from the galaxy to collapse and efficiently form stars deep within clouds. We show that this naturally explains the observed drop in star formation rate per unit gas mass in the Milky Way’s Central Molecular Zone and other galaxy centers. The model for a galactic bottleneck to star formation also agrees well with resolved observations of dense gas and star formation in galaxy disks and the properties of local clouds.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".