3D magnetic-field morphology of the Perseus molecular cloud
Bibliographic record
Abstract
Context.Despite recent observational and theoretical advances in mapping the magnetic fields associated with molecular clouds, their three-dimensional (3D) morphology remains unresolved. Multi-wavelength and multi-scale observations will allow us to paint a comprehensive picture of the magnetic fields of these star-forming regions. Aims.We reconstructed the 3D magnetic field morphology associated with the Perseus molecular cloud and compared it with predictions of cloud-formation models. These cloud-formation models predict a bending of magnetic fields associated with filamentary molecular clouds. We compared the orientation and direction of this field bending with our 3D magnetic-field view of the Perseus cloud. Methods.We used previous line-of-sight and plane-of-sky magnetic field observations as well as Galactic magnetic field models to reconstruct the complete 3D magnetic field vectors and morphology associated with the Perseus cloud. Results.We approximated the 3D magnetic field morphology of the cloud as a concave arc that points in the decreasing longitude direction in the plane of the sky (from our point of view). This field morphology preserves a memory of the Galactic magnetic field. In order to compare this morphology to cloud-formation model predictions, we assume that the cloud retains a memory of its most recent interaction. After incorporating velocity observations, we find that the line-of-sight magnetic field observations are consistent with predictions of shock-cloud-interaction models. Conclusions.To our knowledge, this is the first time that the 3D magnetic fields of a molecular cloud have been reconstructed. We find the 3D magnetic field morphology of the Perseus cloud to be consistent with the predictions of the shock-cloud-interaction model that describes the formation mechanism of filamentary molecular clouds.
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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 | 0.000 |
| 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) | 0.002 | 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".