The chaotic wind of WR 40 as probed by BRITE
Bibliographic record
Abstract
ABSTRACT Among Wolf–Rayet stars, those of subtype WN8 are the intrinsically most variable. We have explored the long-term photometric variability of the brightest known WN8 star, WR 40, through four contiguous months of time-resolved, single-passband optical photometry with the BRIght Target Explorer nanosatellite mission. The Fourier transform of the observed light curve reveals that the strong light variability exhibited by WR 40 is dominated by many randomly triggered, transient, low-frequency signals. We establish a model in which the whole wind consists of stochastic clumps following an outflow visibility promptly rising to peak brightness upon clump emergence from the optically thick pseudo-photosphere in the wind, followed by a gradual decay according to the right-half of a Gaussian. Free electrons in each clump scatter continuum light from the star. We explore a scenario where the clump size follows a power-law distribution, and another one with an ensemble of clumps of constant size. Both scenarios yield simulated light curves morphologically resembling the observed light curve remarkably well, indicating that one cannot uniquely constrain the details of clump size distribution with only a photometric light curve. Nevertheless, independent evidence favours a negative-index power law, as seen in many other astrophysical turbulent media.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".