THE DISRUPTION OF GIANT MOLECULAR CLOUDS BY RADIATION PRESSURE & THE EFFICIENCY OF STAR FORMATION IN GALAXIES
Bibliographic record
Abstract
Star formation is slow in the sense that the gas consumption time is much longer than the dynamical time. It is also inefficient; star formation in local galaxies takes place in giant molecular clouds (GMCs), but the fraction of a GMC converted to stars is very small, GMC ∼ 5%. In luminous starbursts, the GMC lifetime is shorter than the main-sequence lifetime of even the most massive stars, so that supernovae can play no role in GMC disruption. We investigate the disruption of GMCs across a wide range of galaxies from normal spirals to the densest starbursts; we take into account the effects of H ii gas pressure, shocked stellar winds, protostellar jets, and radiation pressure produced by the absorption and scattering of starlight on dust grains. In the Milky Way, a combination of three mechanisms—jets, H ii gas pressure, and radiation pressure—disrupts the clouds. In more rapidly star-forming galaxies such as "clump" galaxies at high-redshift, ultra-luminous infrared galaxies (ULIRGs), and submillimeter galaxies, radiation pressure dominates natal cloud disruption. We predict the presence of ∼10–20 clusters with masses ∼10 7 M ☉ in local ULIRGs such as Arp 220 and a similar number of clusters with M * ∼ 10 8 M ☉ in high redshift clump galaxies; submillimeter galaxies will have even more massive clusters. We find that GMC = π G Σ GMC c /(2( L / M * )) for GMCs that are optically thin to far-infrared radiation, where Σ GMC is the GMC gas surface density. The efficiency in optically thick systems continues to increase with Σ GMC , but more slowly, reaching ∼35% in the most luminous starbursts. The disruption of bubbles by radiation pressure stirs the interstellar medium (ISM) to velocities of ∼10 km s −1 in normal galaxies and to ∼100 km s −1 in ULIRGs like Arp 220, consistent with observations. Thus, radiation pressure may play a dominant dynamical role in the ISM of star-forming galaxies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".