Helical magnetic fields in molecular clouds?
Bibliographic record
Abstract
Context.Magnetic fields pervade in the interstellar medium (ISM) and are believed to be important in the process of star formation, yet probing magnetic fields in star formation regions is challenging.Aims.We propose a new method to use Faraday rotation measurements in small-scale star forming regions to find the direction and magnitude of the component of magnetic field along the line of sight. We test the proposed method in four relatively nearby regions of Orion A, Orion B, Perseus, and California. Methods.We use rotation measure data from the literature. We adopt a simple approach based on relative measurements to estimate the rotation measure due to the molecular clouds over the Galactic contribution. We then use a chemical evolution code along with extinction maps of each cloud to find the electron column density of the molecular cloud at the position of each rotation measure data point. Combining the rotation measures produced by the molecular clouds and the electron column density, we calculate the line-of-sight magnetic field strength and direction. Results.In California and Orion A, we find clear evidence that the magnetic fields at one side of these filamentary structures are pointing towards us and are pointing away from us at the other side. Even though the magnetic fields in Perseus might seem to suggest the same behavior, not enough data points are available to draw such conclusions. In Orion B, as well, there are not enough data points available to detect such behavior. This magnetic field reversal is consistent with a helical magnetic field morphology. In the vicinity of available Zeeman measurements in OMC-1, OMC-B, and the dark cloud Barnard 1, we find magnetic field values of − 23 ± 38 μG, − 129 ± 28 μG, and 32 ± 101 μG, respectively, which are in agreement with the Zeeman measurements.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".