3D radiative transfer modelling and virial analysis of starless cores in the B10 region of the Taurus molecular cloud
Bibliographic record
Abstract
ABSTRACT Low-mass stars like our Sun begin their evolution within cold (10 K) and dense (∼105 cm−3) cores of gas and dust. The physical structure of starless cores is best probed by thermal emission of dust grains. We present a high-resolution dust continuum study of the starless cores in the B10 region of the Taurus Molecular Cloud. New observations at 1.2 and 2.0 mm (12 and 18 arcsec resolution) with the NIKA2 instrument on the IRAM 30m have probed the inner regions of 14 low-mass starless cores. We perform sophisticated 3D radiative transfer modelling for each of these cores through the radiative transfer framework pandora, which utilizes RADMC-3D. Model best-fits constrain each cores’ central density, density slope, aspect ratio, opacity, and interstellar radiation field strength. These ‘typical’ cores in B10 span central densities from 5 × 104 to 1 × 106 cm−3, with a mean value of 2.6 × 105 cm−3. We find the dust opacity laws assumed in the 3D modelling, as well as the estimates from Herschel, have dust emissivity indices, β’s, on the lower end of the distribution constrained directly from the NIKA2 maps, which averages to β = 2.01 ± 0.48. From our 3D density structures and archival NH3 data, we perform a self-consistent virial analysis to assess each core’s stability. Ignoring magnetic field contributions, we find nine out of the 14 cores (64 per cent) are either in virial equilibrium or are bound by gravity and external pressure. To push the bounded cores back to equilibrium, an effective magnetic field difference of only ∼15 $\mu$G is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".