Dust Polarization toward Embedded Protostars in Ophiuchus with ALMA. III. Survey Overview
Bibliographic record
Abstract
Abstract We present 0.″25 resolution (35 au) ALMA 1.3 mm dust polarization observations for 37 young stellar objects (YSOs) in the Ophiuchus molecular cloud. These data encompass all the embedded protostars in the cloud and several flat-spectrum and Class II objects to produce the largest, homogeneous study of dust polarization on disk scales to date. The goal of this study is to study dust polarization morphologies down to disk scales. We find that 14/37 (38%) of the observed YSOs are detected in polarization at our sensitivity. Nine of these sources have uniform polarization angles, and four sources have azimuthal polarization structure. We find that the sources with uniform polarization tend to have steeper inclinations (>60°) than those with azimuthal polarization (<60°). Overall, the majority (9/14) of the detected sources have polarization morphologies and disk properties consistent with dust self-scattering processes in optically thick disks. The remaining sources may be instead tracing magnetic fields. Their inferred field directions from rotating the polarization vectors by 90° are mainly poloidal or hourglass shaped. We find no evidence of a strong toroidal field component toward any of our disks. For the 23 YSOs that are undetected in polarization, roughly half of them have 3σ upper limits of <2%. These sources also tend to have inclinations <60°, and they are generally compact. Since lower-inclination sources tend to have azimuthal polarization, these YSOs may be undetected in polarization owing to unresolved polarization structure within our beam. We propose that disks with inclinations >60° are the best candidates for future polarization studies of dust self-scattering, as these systems will generally show uniform polarization vectors that do not require very high resolution to resolve. We release the continuum and polarization images for all the sources with this publication. Data from the entire survey can be obtained from Dataverse.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".