SUBMILLIMETER POLARIZATION OF GALACTIC CLOUDS: A COMPARISON OF 350 μm AND 850 μm DATA
Bibliographic record
Abstract
The Hertz and SCUBA polarimeters, working at 350 μm and 850 μm, respectively, have measured the polarized emission in scores of Galactic clouds. Of the clouds in each data set, 17 were mapped by both instruments with good polarization signal-to-noise ratios. We present maps of each of these 17 clouds comparing the dual-wavelength polarization amplitudes and position angles at the same spatial locations. In total number of clouds compared, this is a four-fold increase over previous work. Across the entire data set real position angle differences are seen between wavelengths. While the distribution of ϕ(850)–ϕ(350) is centered near zero (near-equal angles), 64% of data points with high polarization signal-to-noise ( P ⩾ 3σ p ) have |ϕ(850)–ϕ(350)| > 10°. Of those data with small changes in position angle (⩽10°) the median ratio of the polarization amplitudes is P (850)/ P (350) = 1.7 ± 0.6. This value is consistent with previous work performed on smaller samples and models that require mixtures of different grain properties and polarization efficiencies. Along with the polarization data we have also compiled the intensity data at both wavelengths; we find a trend of decreasing polarization with increasing 850-to-350 μm intensity ratio. All the polarization and intensity data presented here (1699 points in total) are available in electronic format.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".