Detection of two power-law tails in the probability distribution functions of massive GMCs
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We report the novel detection of complex high column density tails in the probability distribution functions (PDFs) for three high-mass star-forming regions (CepOB3, MonR2, NGC 6334), obtained from dust emission observed with Herschel. The low column density range can be fitted with a lognormal distribution. A first power-law tail starts above an extinction (AV) of ∼6–14. It has a slope of α = 1.3–2 for the ρ ∝ r−α profile for an equivalent density distribution (spherical or cylindrical geometry), and is thus consistent with free-fall gravitational collapse. Above AV ∼40, 60, and 140, we detect an excess that can be fitted by a flatter power-law tail with α > 2. It correlates with the central regions of the cloud (ridges/hubs) of size ∼1 pc and densities above 104 cm−3. This excess may be caused by physical processes that slow down collapse and reduce the flow of mass towards higher densities. Possible are: (1) rotation, which introduces an angular momentum barrier, (2) increasing optical depth and weaker cooling, (3) magnetic fields, (4) geometrical effects, and (5) protostellar feedback. The excess/second power-law tail is closely linked to high-mass star-formation though it does not imply a universal column density threshold for the formation of (high-mass) stars.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it