Submillimetre polarimetric observations of magnetic fields in star-forming regions
Bibliographic record
Abstract
Abstract Submillimetre imaging polarimetry is one of the most powerful tools at present for studying magnetic fields in star-forming regions, and the only way to gain significant information on the structure of these fields. We present analysis of the largest sample (to date) of both high- and low-mass star-forming regions observed using this technique. A variety of magnetic field morphologies are observed, with no single field morphology favoured. Both the continuum emission morphologies and the field morphologies are generally more complex for the high-mass sample than the low-mass sample. The large scale magnetic field (observed with the JCMT; 14″ resolution) of NGC1333 IRAS2 is interpreted to be weak (compared to the energetic contributions due to turbulence) from the random field pattern observed. On smaller scales (observed with the BIMA array; 3″ resolution) the field is observed to be almost radial, consistent with the polarisation nulls in the JCMT data – suggesting that on smaller scales, the field may be more important to the star formation process. An analysis of the magnetic field direction and the jet/outflow axis is also discussed. Cumulative distribution functions of the difference between the mean position angle of the magnetic field vectors and the jet/outflow axis reveal no correlation. However, visual inspection of the maps reveal alignment of the magnetic field and jet/outflow axis in 7 out of 15 high-mass regions and 3 out of 8 low-mass regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".