Statistical Properties of Galactic Starlight Polarization
Bibliographic record
Abstract
We present a statistical analysis of Galactic interstellar polarization from the largest compilation available of starlight data. The data comprises ~9300 stars, of which we have selected ~5500 for our analysis. We find a nearly linear growth of mean polarization degree with extinction. The amplitude of this correlation shows that interstellar grains are not fully aligned with the Galactic magnetic field, which can be interpreted as the effect of a large random component of the field. In agreement with earlier studies of more limited scope, we estimate the ratio of the uniform to the random plane-of-the-sky components of the magnetic field to be B u / B r ≈ 0.8. Moreover, a clear correlation exists between polarization degree and polarization angle that provides evidence that the magnetic field geometry follows Galactic structures on large scales. The angular power spectrum C ℓ of the starlight polarization degree for Galactic plane data (| b | < 10°) is consistent with a power law, C ℓ ∝ ℓ -1.5 (where ℓ ≈ 180°/θ is the multipole order), for all angular scales θ ≳ 10'. An investigation of sparse and inhomogeneous sampling of the data shows that the starlight data analyzed traces an underlying polarized continuum that has the same power-spectrum slope, C ℓ ∝ ℓ -1.5 . Our findings suggest that starlight data can be safely used for the modeling of Galactic polarized continuum emission at other wavelengths.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".