Clumping in the Winds of Wolf–Rayet Stars
Bibliographic record
Abstract
Abstract We attempt to determine the driver for clumping in hot-star winds by extending the measure of the spectral variability level of Galactic Wolf–Rayet stars to by far the hottest known among them, the WN2 star WR 2 and the WO2 stars WR 102 and WR 142. These three stars have T ⋆ = 140 and 200 kK, the last two being well above the bulk of WR stars with T ⋆ ∼ 40–120 kK. This full temperature range for WR stars is much broader than that of their O-star progenitors (∼30–50 kK), so is better suited to look for any temperature dependence of wind clumping. We have obtained multiple observations with high signal-to-noise, moderate-resolution spectroscopy in search of small-scale variability in the strong emission lines from the dense winds of these three extreme stars, and find a very low-level of variability in both stars. Temperature and terminal velocity are correlated, so faster winds show a lower variability, though this trend goes against any predictions made involving Line Deshadowing Instability (LDI) only, implying that instabilities intrinsic to LDI are not the main source of wind clumping. Instead, it could be taken as support for the suggestion that clumps are caused by a subsurface convection zone (SSCZ) at T ∼ 170 kK, since such an SSCZ would have little opportunity to operate under the hydrostatic surface of these hottest WR stars. It is still possible, however, that an SSCZ-related driver could interact with nonlinear line instability effects to enhance or possibly even produce clumps.
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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 | 0.000 | 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".