Mapping the Galactic magnetic field orientation and strength in three dimensions
Bibliographic record
Abstract
ABSTRACT The mapping of the Galactic magnetic field (GMF) in three dimensions is essential to comprehend various astrophysical processes that occur within the Milky Way. This study endeavours to map the GMF by utilizing the latest MM2 technique, the velocity gradient technique (VGT), the column density variance approach, and the Galactic Arecibo L-band Feed Array HI survey of neutral hydrogen (H i) emission. The MM2 and VGT methods rely on an advanced understanding of magnetohydrodynamics turbulence to determine the plane-of-the-sky magnetic field strength and orientation, respectively. The H i emission data, combined with the Galactic rotational curve, give us the distribution of H i gas throughout the Milky Way. By combining these two techniques, we map the GMF orientation and strength, as well as the Alfvén Mach number MA in 3D for a low-galactic latitude (b < 30o) region close to the Perseus Arm. The analysis of column density variance gives the sonic Mach number Ms distribution. The results of this study reveal the sub-Alfvénic and subsonic (or trans-sonic) nature of the H i gas. The variation of mean MA along the line of sight approximately ranges from 0.6 to 0.9, while that of mean Ms is from 0.2 to 1.5. The mean magnetic field strength varies from 0.5 to 2.5 µG exhibiting a decreasing trend towards the Galaxy’s outskirt. This work provides a new avenue for mapping the GMF, especially the magnetic field strength, in 3D. We discuss potential synergetic applications with other approaches.
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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 | 0.002 | 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) | 0.001 | 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".