Light variations due to the line-driven wind instability and wind blanketing in O stars
Bibliographic record
Abstract
A small fraction of the radiative flux emitted by hot stars is absorbed by their winds and redistributed towards longer wavelengths. This effect, which leads also to the heating of the stellar photosphere, is termed wind blanketing. For stars with variable winds, the effect of wind blanketing may lead to the photometric variability. We have studied the consequences of line driven wind instability and wind blanketing for the light variability of O stars. We combined the results of wind hydrodynamic simulations and of global wind models to predict the light variability of hot stars due to the wind blanketing and instability. The wind instability causes stochastic light variability with amplitude of the order of tens of millimagnitudes and a typical timescale of the order of hours for spatially coherent wind structure. The amplitude is of the order of millimagnitudes when assuming that the wind consists of large number of independent concentric cones. The variability with such amplitude is observable using present space borne photometers. We show that the simulated light curve is similar to the light curves of O stars obtained using BRITE and CoRoT satellites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".