Magnetic Field Structure in Spheroidal Star-forming Clouds. II. Estimating Field Structure from Observed Maps
Bibliographic record
Abstract
This paper presents models to estimate the structure of density and magnetic field strength in spheroidal condensations from maps of their column density and their polarization of magnetically aligned dust grains. The density model is obtained by fitting a column density map with an embedded p = 2 Plummer spheroid of any aspect ratio and inclination. The magnetic properties are based on the density model, the Davis-Chandrasekhar-Fermi (DCF) model of Alfvénic fluctuations, and the spheroid flux freezing (SFF) model of mass and flux conservation in Paper I. The field strength model has the resolution of the column density map, which is finer than the resolution of the DCF estimate of field strength. The models are applied to ALMA observations of the envelope of the protostar BHR 71 IRS1. Column density fits give the density model from (2.0 ± 0.4) × 105 to (7 ± 1) × 107 cm-3. The density model predicts the field directions map, which fits the polarization map best within 1100 au, with standard deviation of angle differences of 17°. In this region, the DCF mean field strength is 0.7 ± 0.2 mG, and the envelope mass is supercritical, with a ratio of mass to magnetic critical mass of 1.5 ± 0.4. The SFF field strength profile scales with the DCF field strength from 60 ± 10 μG to 3 ± 1 mG. The spatial resolution of the SFF field strength estimate is finer than the DCF resolution by a factor of ∼7, and the peak SFF field strength exceeds the DCF field strength by a factor of ∼4.
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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 | 0.000 |
| 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 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".