Projection-angle effects when “observing” a turbulent magnetized collapsing molecular cloud
Bibliographic record
Abstract
Context. Interstellar magnetic fields are thought to play a fundamental role in the evolution of star-forming regions. Polarized thermal dust emission serves as a key probe for understanding the structure of the plane-of-the-sky component of the magnetic field in such regions. However, inclination effects can potentially significantly influence the apparent morphology of the magnetic field and lead to erroneous conclusions regarding its dynamical importance. Aims. Our aim is to investigate how projection-angle effects impact dust polarization maps and to explore new ways for accessing the inclination angle of the mean component of the magnetic field with respect to the plane of the sky. Methods. We post-processed a 3D ideal magnetohydrodynamic simulation of a turbulent collapsing molecular cloud at a central density of 105 cm−3, when the cloud has flattened perpendicular to the mean magnetic field. We produced synthetic dust polarization measurements under various projection angles, ranging from “face-on” (i.e., viewed along the mean magnetic field direction) to “edgeon” (perpendicular to the mean magnetic field direction). Additionally, we used synthetic position-position-velocity (PPV) data cubes from the CO (J = 1 → 0) transition, presented in a companion paper. Results. The projected magnetic-field morphology is found to be highly affected by the projection angle with the hourglass morphology being clearly visible only for projection angles close to “edge-on”. We find that the direction of the apparent “flow” between successive velocity channels in the simulated PPV data cubes shows an increasing correlation with the synthetic dust polarization observations, as the cloud is observed closer to an “edge-on” orientation. Based on this property, we have developed a new method to probe the inclination angle of the magnetic field relative to the plane of the sky. We validated our approach by generating additional synthetic data (PPV cubes and polarization maps) at an earlier stage of the cloud’s evolution. We demonstrate an excellent quantitative agreement between the derived inclination angle and the true observational angle. We note that our method is relevant only for collapsing 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".