Bibliographic record
Abstract
Among the most spectacular variable stars are the luminous blue variables (LBVs), which can show three types of variability. The LBV phase of evolution is poorly understood, and the driving mechanisms for the variability are not known. The most common type of variability, the S Dor instability, occurs on time-scales of tens of years. During an S Dor outburst, the visual magnitude of the star increases, while the bolometric magnitude stays approximately constant. In this work, we investigate pulsation as a possible trigger for the S Dor-type outbursts. We calculate the pulsations of envelope models using a non-linear hydrodynamic code including a time-dependent convection treatment. We initialize the pulsation in the hydrodynamic model based on linear non-adiabatic calculations. Pulsation properties for a full grid of models from 20 to 85 M⊙ were calculated, and in this paper, we focus on the few models that show either long-period pulsations or outburst-like behaviour, with photospheric radial velocities reaching 70–80 km s−1. At the present time, our models cannot follow mass-loss, so once the outburst event begins, our simulations are terminated. Our results show that pulsations alone are not able to drive enough surface expansion to eject the outer layers. However, the outbursts and long-period pulsations discussed here produce large variations in effective temperature and luminosity, which are expected to produce large variations in the radiatively driven mass-loss rates.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".