Constraints on clumps in the representative wind of the WN8 Wolf–Rayet star HD 96548 = WR 40 with simultaneous broad-band light and linear-polarization variability
Bibliographic record
Abstract
ABSTRACT We report precision ground-based broad-band optical intensity and linear-polarization light curves for the sky’s brightest WN8 star, WR 40. WN8 stars are notorious for their high level of variability, stemming from stochastic clumps in their strong winds that are slower and less hot than the winds of most other Wolf–Rayet (WR) stars. We confirm previous results that many WR stars display an amplitude of variability that is an order of magnitude higher in photometry than in polarimetry. For the first time, the unique nature of near simultaneity of our photometric and polarimetric observations of WR 40 allows us to check whether the two types of variability show correlated behaviour, of which we find none. Assuming simple temporal functions for the brightness and polarization of individual clumps, a model for simulated light curves is found to reproduce the properties of the observations, specifically the absence of correlation between photometric and polarimetric variations, the ratio of standard deviations for photometric and polarimetric variability, and the ratio of the average intrinsic polarization relative to its standard deviation. Mapping the solution for time variability to a spatial coordinate suggests that the wind clumps of WR 40 are free-free emitting in addition to light scattering.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".