High‐Mass Star Formation. II. The Mass Function of Submillimeter Clumps in M17
Bibliographic record
Abstract
We have mapped a ~5.5 × 5.5 pc portion of the M17 massive star-forming region in both 850 and 450 μm dust continuum emission using the Submillimeter Common-User Bolometer Array (SCUBA) on the James Clerk Maxwell Telescope (JCMT). The maps reveal more than 100 dusty clumps with deconvolved linear sizes of ~0.05-0.2 pc and masses of ~0.8-120 M ☉ , most of which are not associated with known mid-infrared point sources. Fitting the clump mass function with a double power law gives a mean power-law exponent of α high = -2.4 ± 0.3 for the high-mass power law, consistent with the exponent of the Salpeter stellar mass function. We show that a lognormal clump mass distribution with a peak at ~4 M ☉ produces as good a fit to the clump mass function as does a double power law. This 4 M ☉ peak mass is well above the peak masses of both the stellar initial mass function and the mass function of clumps in low-mass star-forming regions. Despite the difference in intrinsic mass scale, the shape of the M17 clump mass function appears to be consistent with the shape of the core mass function in low-mass star-forming regions. Thus, we suggest that the clump mass function in high-mass star-forming regions may be a scaled up version of that in low-mass regions, instead of its extension to higher masses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".