A Study of the Stochastic Photometric Variability in the Winds of Galactic Wolf–Rayet Stars
Bibliographic record
Abstract
Abstract In order to explore how the ubiquitous short-term stochastic variability in the photometric observations of Wolf–Rayet (WR) stars is related to various stellar characteristics, we examined a sample of 50 Galactic WR stars using 122 lightcurves obtained by the BRIght Target Explorer-Constellation, Transiting Exoplanet Survey Satellite and Microvariability and Oscillations of Stars satellites. We found that the periodograms resulting from a discrete Fourier transform of all our detrended lightcurves are characterized by a forest of random peaks showing an increase in power starting from ∼0.5 day−1 down to ∼0.1 day−1. After fitting the periodograms with a semi-Lorentzian function representing a combination of white and red noise, we investigated possible correlations between the fitted parameters and various stellar and wind characteristics. Seven correlations were observed, the strongest and only significant one being between the amplitude of variability, α 0, observed for hydrogen-free WR stars, while WNh stars exhibit correlations between α 0 and the stellar temperature, T *, and also between the characteristic frequency of the variations, ν char, and both T * and v ∞. We report that stars observed more than once show significantly different variability parameters, indicating an epoch-dependent measurement. We also find that the observed characteristic frequencies for the variations generally lie between − 0.5 < log 10 ν char < 0.5 , and that the values of the steepness of the amplitude spectrum are typically found in the range − 0.1 < log 10 γ < 0.5 . We discuss various physical processes that can lead to this correlation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".