X-Shooting ULLYSES: Massive stars at low metallicity
Bibliographic record
Abstract
Context. The winds of massive stars have a significant impact on stellar evolution and on the surrounding medium. The maximum speed reached by these outflows, the terminal wind speed v∞, is a global wind parameter and an essential input for models of stellar atmospheres and feedback. With the arrival of the ULLYSES programme, a legacy UV spectroscopic survey with the Hubble Space Telescope, we have the opportunity to quantify the wind speeds of massive stars at sub-solar metallicity (in the Large and Small Magellanic Clouds, 0.5 Z⊙ and 0.2 Z⊙, respectively) at an unprecedented scale. Aims. We empirically quantify the wind speeds of a large sample of OB stars, including supergiants, giants, and dwarfs at sub-solar metallicity. Using these measurements, we investigate trends of v∞ with a number of fundamental stellar parameters, namely effective temperature (Teff), metallicity (Z), and surface escape velocity vesc. Methods. We empirically determined v∞ for a sample of 149 OB stars in the Magellanic Clouds either by directly measuring the maximum velocity shift of the absorption component of the C IV λλ1548–1550 line profile, or by fitting synthetic spectra produced using the Sobolev with exact integration method. Stellar parameters were either collected from the literature, obtained using spectral-type calibrations, or predicted from evolutionary models. Results. We find strong trends of v∞ with Teff and vesc when the wind is strong enough to cause a saturated P Cygni profile in C IV λλ1548–1550. We find evidence for a metallicity dependence on the terminal wind speed v∞ ∝ Z0.22±0.03 when we compared our results to previous Galactic studies. Conclusions. Our results suggest that Teff rather than vesc should be used as a straightforward empirical prediction of v∞ and that the observed Z dependence is steeper than suggested by earlier works.
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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 | 0.001 |
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".