Wolf--Rayet binaries in the Magellanic Clouds and implications for massive-star evolution -- I. Small Magellanic Cloud
Bibliographic record
Abstract
We have carried out an intensive spectroscopic campaign to search for binaries via periodic radial velocity (RV) variations among all the nitrogen-rich WN Wolf—Rayet (WR) stars in the Small Magellanic Cloud (SMC), and all WNE stars in the Large Magellanic Cloud (LMC). We present in this first paper the results for the SMC. Along with the results of Bartzakos et al. on the only carbon/oxygen-rich WR star (AB8, WO4+O4), the whole WR population of the SMC (11 stars) has now been investigated intensively for periodic RV variability. We have also retrieved time-dependent photometric data in the public domain from the OGLE and MACHO projects, and X-ray data from the ROSAT and Chandra archives, to provide additional constraints on the binary character. Contrary to theoretical expectations that predict a virtually 100 per cent binary frequency in the SMC, we find a normal (∼40 per cent) WR binary frequency in this galaxy. We also find the clear presence of hydrogen in the winds of the single WR stars in the SMC, even for the stars with an early spectral subtype. We discuss the possible reasons and implications of this for stellar evolution of massive stars in such a low-metallicity environment, e.g. the influence of rotation versus the necessity of a very high initial mass of the progenitors for single stars, and the possible past occurrence of Roche lobe overflow for binaries.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".