CONSTRAINT ON THE POLARIZATION OF ELECTRIC DIPOLE EMISSION FROM SPINNING DUST
Bibliographic record
Abstract
Planck results have revealed that the electric dipole emission from polycyclic aromatic hydrocarbons (PAHs) is the most reliable explanation for the anomalous microwave emission that interferes with cosmic microwave background (CMB) radiation experiments. The emerging question is to what extent this emission component contaminates the polarized CMB radiation. We present constraints on polarized dust emission for the model of grain-size distribution and grain alignment that best fits the observed extinction and polarization curves. Two stars with a prominent polarization feature at λ = 2175 Å—HD 197770 and HD 147933-4—are chosen for our study. For HD 197770, we find that the model with aligned silicate grains plus weakly aligned PAHs can successfully reproduce the 2175 Å polarization feature; in contrast, for HD 147933-4, we find that the alignment of only silicate grains can account for that feature. The alignment function of PAHs for the best-fit model to the HD 197770 data is used to constrain polarized spinning dust emission. We find that the degree of polarization of spinning dust emission is about 1.6% at frequency ν ≈ 3 GHz and declines to below 0.9% for ν > 20 GHz. We also predict the degree of polarization of thermal dust emission at 353 GHz to be P em ≈ 11% and 14% for the lines of sight to the HD 197770 and HD 147933-4 stars, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".