STAR FORMATION IN MASSIVE CLUSTERS VIA BONDI ACCRETION
Bibliographic record
Abstract
Essentially all stars form in giant molecular clouds (GMCs). However, inside GMCs, most of the gas does not participate in star formation; rather, denser gas accumulates in clumps in the GMC, with the bulk of the stars in a given GMC forming in a few of the most massive clumps. In the Milky Way, these clumps have masses M cl ≲ 5 × 10 −2 of the GMC, radii r cl ∼ 1 pc, and free-fall times τ cl ∼ 2 × 10 5 yr. We show that clumps inside GMCs should accrete at a modified Bondi accretion rate, which depends on clump mass as . This rate is initially rather slow, usually slower than the initial star formation rate inside the clump (we adopt the common assumption that inside the clump, , with ff ≈ 0.017). However, after ∼2 GMC free-fall times τ GMC , the clump accretion rate accelerates rapidly; formally, the clump can accrete the entire GMC in ∼3τ GMC . At the same time, the star formation rate accelerates, tracking the Bondi accretion rate. If the GMC is disrupted by feedback from the largest clump, half the stars in that clump form in the final τ GMC before the GMC is disrupted. The theory predicts that the distribution of effective star formation rates, measured per GMC free-fall time, is broad, ranging from ∼0.001 up to 0.1 or larger and that the mass spectrum of star clusters is flatter than that of clumps, consistent with observations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".