Polarization light curve modelling of corotating interaction regions in the wind of the Wolf–Rayet star WR 6
Bibliographic record
Abstract
The intriguing WN4b star WR 6 has been known to display epoch-dependent spectroscopic, photometric and polarimetric variability for several decades. In this paper, we set out to verify if a simplified analytical model in which corotating interaction regions (CIRs) threading an otherwise spherical wind is able to reproduce the many broad-band continuum light curves from the literature with a reasonable set of parameters. We modified the optically thin model developed by Ignace, St-Louis & Proulx-Giraldeau to approximately account for multiple scattering and used it to fit 13 separate data sets of this star. By including two CIRs in the wind, we obtained reasonable fits for all data sets with coherent values for the inclination of the rotation axis (i0 = 166°) and for its orientation in the plane of the sky, although in the latter case we obtained two equally acceptable values (ψ = 63° and 152°) from the polarimetry. Additional line profile variation simulations using the Sobolev approximation for the line transfer allowed us to eliminate the ψ = 152° solution. With the adopted configuration (i0 = 166° and ψ = 63°), we were able to reproduce all data sets relatively well with two CIRs located near the stellar equator and always separated by ∼90° in longitude. The epoch dependence comes from the fact that these CIRs migrate along the surface of the star. Density contrasts smaller than a factor of 2 and large opening angles for the CIR (|$\beta \gtrapprox 35^{\circ}$|) were found to best reproduce the type of spectroscopic variability reported in the literature.
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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 | 0.000 |
| 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 | 0.001 | 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".