The properties of bound and unbound molecular cloud populations formed in galactic disc simulations
Bibliographic record
Abstract
We explore the effect of galactic environment on properties of molecular clouds. Using clouds formed in a large-scale galactic disc simulation, we measure the observable properties from synthetic column density maps. We confirm that a significant fraction of unbound clouds forms naturally in a galactic disc environment and that a mixed population of bound and unbound clouds can match observed scaling relations and distributions for extragalactic molecular clouds. By dividing the clouds into inner and outer disc populations, we compare their distributions of properties and test whether there are statistically significant differences between them. We find that clouds in the outer disc have lower masses, sizes, and velocity dispersions as compared to those in the inner disc for reasonable choices of the inner/outer boundary. We attribute the differences to the strong impact of galactic shear on the disc stability at large galactocentric radii. In particular, our Toomre analysis of the disc shows a narrowing envelope of unstable masses as a function of radius, resulting in the formation of smaller, lower mass fragments in the outer disc. We also show that the star formation rate is affected by the environment of the parent cloud, and is particularly influenced by the underlying surface density profile of the gas throughout the disc. Our work highlights the strengths of using galaxy–scale simulations to understand the formation and evolution of cloud properties – and the star formation within them – in the context of their environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".