JCMT BISTRO Survey Observations of the Ophiuchus Molecular Cloud: Dust Grain Alignment Properties Inferred Using a Ricean Noise Model
Bibliographic record
Abstract
Abstract The dependence of the polarization fraction p on total intensity I in polarized submillimeter emission measurements is typically parameterized as p ∝ I − α ( α ≤ 1) and used to infer dust grain alignment efficiency in star-forming regions, with an index α = 1 indicating near-total lack of alignment of grains with the magnetic field. In this work, we demonstrate that the non-Gaussian noise characteristics of the polarization fraction may produce apparent measurements of α ∼ 1 even in data with significant signal-to-noise in Stokes Q , U , and I emission, and so with robust measurements of polarization angle. We present a simple model demonstrating this behavior and propose a criterion by which well-characterized measurements of the polarization fraction may be identified. We demonstrate that where our model is applicable, α can be recovered by fitting the p – I relationship with the mean of the Rice distribution without statistical debiasing of the polarization fraction. We apply our model to JCMT BISTRO Survey POL-2 850 μ m observations of three clumps in the Ophiuchus molecular cloud, finding that in the externally illuminated Oph A region, α ≈ 0.34, while in the more isolated Oph B and C, despite their differing star formation histories, α ∼ 0.6–0.7. Our results thus suggest that dust grain alignment in dense gas is more strongly influenced by the incident interstellar radiation field than by star formation history. We further find that grains may remain aligned with the magnetic field at significantly higher gas densities than has previously been believed, thus allowing investigation of magnetic field properties within star-forming clumps and cores.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".