The initial conditions of high-mass star formation: radiative transfer models of IRDCs seen in the<i>Herschel</i>Hi-GAL survey
Bibliographic record
Abstract
The densest infrared dark clouds (IRDCs) may represent the earliest observable stage of high-mass star formation. These clouds are very cold, hence they emit mainly at far-infrared and sub-mm wavelengths. For the first time, Herschel has provided multi-wavelength, spatially resolved observations of cores within IRDCs, which, when combined with radiative transfer modelling, can constrain their properties, such as mass, density profile and dust temperature. We use a 3D, multi-wavelength Monte Carlo radiative transfer code to model in detail the emission from six cores in three typical IRDCs seen in the Hi-GAL survey (G030.50+00.95, G031.03+00.26 and G031.03+00.76), and thereby to determine the properties of these cores and compare them with their low-mass equivalents. We found masses ranging from 90 to 290 M⊙ with temperatures from 8 to 11 K at the centre of each core and 18 to 28 K at the surface. The maximum luminosity of an embedded star within each core was calculated, and we rule out the possibility of significant high mass star formation having yet occurred in three of our cores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".