Polarization simulations of stellar wind bow shock nebulae – II. The case of dust scattering
Bibliographic record
Abstract
ABSTRACT We study the polarization produced by scattering from dust in a bow shock-shaped region of enhanced density surrounding a stellar source, using the Monte Carlo radiative transfer code SLIP. Bow shocks are structures formed by the interaction of the winds of fast-moving stars with the interstellar medium. Our previous study focused on the polarization produced in these structures by electron scattering; we showed that polarization is highly dependent on inclination angle and that multiple scattering changes the shape and degree of polarization. In contrast to electron scattering, dust scattering is wavelength-dependent, which changes the polarization behaviour. Here, we explore different dust particle sizes and compositions and generate polarized spectral energy distributions for each case. We find that the polarization spectral energy distribution behaviour depends on the dust composition and grain size. Including dust emission leads to polarization changes with temperature at higher optical depth in ways that are sensitive to the orientation of the bow shock. In various scenarios and under certain assumptions, our simulations can constrain the optical depth and dust properties of resolved and unresolved bow shock-shaped scattering regions. Constraints on optical depth can provide estimates of local interstellar medium density for observed bow shocks. We also study the impact of dust grains filling the region between the star and bow shock. We see that as the density of dust between the star and bow shock increases, the resulting polarization is suppressed for all the optical depth regimes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".