X-Shooting ULLYSES: Massive stars at low metallicity
Bibliographic record
Abstract
Context. Mass loss governs the evolution of massive stars and shapes the stellar surroundings. To quantify the impact of the stellar winds, we need to know the exact mass-loss rates; however, empirical constraints on the rates are hampered by limited knowledge of their small-scale wind structure, also referred to as ‘wind clumping’. Aims. We aim to improve empirical constraints on the mass loss of massive stars by investigating the clumping properties of their winds, in particular, the relation between stellar parameters and wind structure. Methods. We analysed the optical and ultraviolet spectra of 25 O-type giants and supergiants in the Large Magellanic Cloud, using the model atmosphere code FASTWIND and a genetic algorithm. We derived the stellar and wind parameters, including detailed clumping properties, such as the amount of clumping, the density of the interclump medium, velocity–porosity of the medium, and wind turbulence. Results. We obtained stellar and wind parameters for 24 of our sample stars and found that the winds are highly clumped, with an average clumping factor of 〈fcl〉 = 33 14, an interclump density factor of 〈fic〉 = 0.2 0.1, and moderate-to-strong velocity-porosity effects of 〈fvel〉 = 0.6 0.2. The scatter around the average values of the wind-structure parameters is large. With the exception of a significant, positive correlation between the interclump density factor and mass loss, we find no dependence of clumping parameters on the mass-loss rate or stellar properties. Conclusions. In the luminosity range we investigate here, the empirical and theoretical mass-loss rates both have a scatter of about 0.5 dex (or a factor 3). Within this uncertainty, the empirical rates and theoretical predictions are in agreement. The origin of the scatter of the empirically inferred mass-loss rates requires further investigation. It is possible that our description of wind clumping is still not sufficient to capture effects of the structured wind, which could contribute to the scatter.
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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 | 0.001 | 0.001 |
| 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".