Environmental dependence of the molecular cloud lifecycle in 54 main-sequence galaxies
Bibliographic record
Abstract
ABSTRACT The processes of star formation and feedback, regulating the cycle of matter between gas and stars on the scales of giant molecular clouds (GMCs; ∼100 pc), play a major role in governing galaxy evolution. Measuring the time-scales of GMC evolution is important to identify and characterize the specific physical mechanisms that drive this transition. By applying a robust statistical method to high-resolution CO and narrow-band H α imaging from the PHANGS survey, we systematically measure the evolutionary timeline from molecular clouds to exposed young stellar regions on GMC scales, across the discs of an unprecedented sample of 54 star-forming main-sequence galaxies (excluding their unresolved centres). We find that clouds live for about 1−3 GMC turbulence crossing times (5−30 Myr) and are efficiently dispersed by stellar feedback within 1−5 Myr once the star-forming region becomes partially exposed, resulting in integrated star formation efficiencies of 1−8 per cent. These ranges reflect physical galaxy-to-galaxy variation. In order to evaluate whether galactic environment influences GMC evolution, we correlate our measurements with average properties of the GMCs and their local galactic environment. We find several strong correlations that can be physically understood, revealing a quantitative link between galactic-scale environmental properties and the small-scale GMC evolution. Notably, the measured CO-visible cloud lifetimes become shorter with decreasing galaxy mass, mostly due to the increasing presence of CO-dark molecular gas in such environment. Our results represent a first step towards a comprehensive picture of cloud assembly and dispersal, which requires further extension and refinement with tracers of the atomic gas, dust, and deeply embedded stars.
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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 | 0.000 | 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".