The W43-MM1 mini-starburst ridge, a test for star formation efficiency models
Bibliographic record
Abstract
Context. Star formation efficiency (SFE) theories are currently based on statistical distributions of turbulent cloud structures and a simple model of star formation from cores. They remain poorly tested, especially at the highest densities. Aims. We investigate the effects of gas density on the SFE through measurements of the core formation efficiency (CFE). With a total mass of ~2 × 104 M⊙, the W43-MM1 ridge is one of the most convincing candidate precursors of Galactic starburst clusters and thus one of the best places to investigate star formation. Methods. We used high-angular resolution maps obtained at 3 mm and 1 mm within the W43-MM1 ridge with the IRAM Plateau de Bure Interferometer to reveal a cluster of 11 massive dense cores, and, one of the most massive protostellar cores known. A Herschel column density image provided the mass distribution of the cloud gas. We then measured the “instantaneous” CFE and estimated the SFE and the star formation rate (SFR) within subregions of the W43-MM1 ridge. Results. The high SFE found in the ridge (~6% enclosed in ~8 pc3) confirms its ability to form a starburst cluster. There is, however, a clear lack of dense cores in the eastern part of the ridge, which may be currently assembling. The CFE and the SFE are observed to increase with volume gas density, while the SFR per free fall time steeply decreases with the virial parameter, αvir. Statistical models of the SFR may describe the outskirts of the W43-MM1 ridge well, but struggle to reproduce its inner part, which corresponds to measurements at low αvir. It may be that ridges do not follow the log-normal density distribution, Larson relations, and stationary conditions forced in the statistical SFR models.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".