Star Formation Efficiency per Free-fall Time in nearby Galaxies
Bibliographic record
Abstract
Abstract We estimate the star formation efficiency per gravitational free-fall time, , from observations of nearby galaxies with resolution matched to the typical size of a giant molecular cloud. This quantity, , is theoretically important but so far has only been measured for Milky Way clouds or inferred indirectly in a few other galaxies. Using new, high-resolution CO imaging from the Physics at High Angular Resolution in nearby Galaxies-Atacama Large Millimeter Array (PHANGS-ALMA) survey, we estimate the gravitational free-fall time at 60–120 pc resolution, and contrast this with the local molecular gas depletion time in order to estimate . Assuming a constant thickness of the molecular gas layer ( H = 100 pc) across the whole sample, the median value of in our sample is 0.7%. We find a mild scale dependence, with higher measured at coarser resolution. Individual galaxies show different values of , with the median ranging from 0.3% to 2.6%. We find the highest in our lowest-mass targets, reflecting both long free-fall times and short depletion times, though we caution that both measurements are subject to biases in low-mass galaxies. We estimate the key systematic uncertainties, and show the dominant uncertainty to be the estimated line-of-sight (LOS) depth through the molecular gas layer and the choice of star formation tracers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".