Diagnostics from Polarization of Scattered Optical Light from Galactic Infrared Cirrus
Bibliographic record
Abstract
Abstract We propose polarization of scattered optical light from intermediate Galactic latitude infrared cirrus as a new diagnostic to constrain models of interstellar dust and the anisotropic interstellar radiation field (aISRF). For single scattering by a sphere, with Mie scattering phase functions for intensity and polarized intensity for a dust model at a given wavelength (Sloan r and g bands), and with models of anisotropic illumination from the entire sky (represented in HEALPix), we develop the formalism for calculating useful summary parameters for an integrated flux nebula (IFN): the average of the phase function weighted by the illumination, polarization angle (ψ), and polarization fraction (p). To demonstrate the diagnostic discrimination of polarization from scattered light, we report on the effects of different anisotropic illumination models and different dust models on the summary parameters for the Spider IFN. The summary parameters are also sensitive to the IFN location, as we illustrate using FRaNKIE illumination models. For assessing the viability of dust and aISRF models, we find that observations of ψ and p of scattered light are indeed powerful new diagnostics to complement joint modeling of the intensity of scattered light (related to the average phase function) and the intensity of thermal dust emission. However, optically thin IFNs that can be modeled using single scattering are faint and p is not large, as it could be with Rayleigh scattering, and so these observations need to be carried out with care and precision. Results for the Draco nebula compared to the Spider illustrate the challenge.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".