Modelling variable linear polarization produced by Co-rotating Interaction Regions (CIRs) across optical recombination lines of Wolf–Rayet stars
Bibliographic record
Abstract
ABSTRACT Massive star winds are structured both stochastically (‘clumps’) and often coherently (Co-rotation Interaction Regions, or CIRs). Evidence for CIRs threading the winds of Wolf–Rayet (WR) stars arises from multiple diagnostics including linear polarimetry. Some observations indicate changes in polarization position angle across optical recombination emission lines from a WR star wind but limited to blueshifted Doppler velocities. We explore a model involving a spherical wind with a single conical CIR stemming from a rotating star as qualitative proof-of-concept. To obtain a realistic distribution of limb polarization and limb darkening across the pseudo-photosphere formed in the optically thick wind of a WR star, we used Monte Carlo radiative transfer (MCRT). Results are shown for a parameter study. For line properties similar to WR 6 (EZ CMa; HD 50896), combining the MCRT results, a simple model for the CIR, and the Sobolev approximation for the line formation, we were able to reproduce variations in both polarization amplitude and position angle commensurate with observations. Characterizing CIRs in WR winds has added importance for providing stellar rotation periods since the vsin i values are unobtainable because the pseudo-photosphere forms in the wind itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".