Mapping magnetic fields from clouds to cores with PRIMAger
Bibliographic record
Abstract
High-resolution, wide-area mapping of magnetic field geometries within molecular clouds, including the star-forming filaments and cores within them, is crucial to understanding the role of magnetic fields in the star formation process. We, therefore, propose an unbiased survey of star-forming molecular clouds within 0.5 kpc of the Earth in polarized light with the PRIMAger Polarimetry Imager. We will map magnetic fields over entire molecular clouds at linear resolutions of ∼10−3 to 10−2 pc (∼103 to 104 au) in PRIMAger Bands PPI1 to PPI4, thereby resolving magnetic field structure both within individual star-forming filaments and cores and in the most diffuse regions of molecular clouds. These multiwavelength polarimetric observations will allow us to systematically investigate both the wide range of open questions about the role of magnetic fields in star formation and the evolution of the interstellar medium, and interstellar dust grain properties. The time required to map the area observed by the Herschel Gould Belt Survey (160 deg2) to the cirrus confusion limit in polarized light is 170 h. This will give a 5-σ detection of 20% polarized low-density cirrus emission, with surface brightness in polarized intensity of 1.0 to 2.4 MJy/sr across the PRIMAger bands, and will ensure detection of polarized emission at all higher column densities. This time estimate can be simply scaled up to map magnetic fields in a larger sample of molecular clouds, including more distant regions of higher-mass star formation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".