Wolf--Rayet binaries in the Magellanic Clouds and implications for massive-star evolution -- II. Large Magellanic Cloud
Bibliographic record
Abstract
We present in this second paper the results of our intensive spectroscopic campaign to search for binaries via periodic radial-velocity (RV) variations among Wolf—Rayet (WR) stars for the Large Magellanic Cloud (LMC). We observed 61 nitrogen-rich WNE stars in the LMC. Along with the results of Bartzakos, Moffat & Niemela on the carbon/oxygen-rich WR stars, 2/3 of the WR population of the LMC (134 stars in total) has now been investigated for periodic RV variability. We have also retrieved time-dependent photometric data in the public domain from the OGLE and MACHO projects, as well as X-ray data from ROSAT and Chandra satellites, to provide additional constraints on the binary character. For each of our sample stars, we discuss its observational properties: RV variations, (periodic) photometric variability, X-ray luminosity, spectral classification, abundance of hydrogen, runaway status and line-profile variations (LPVs). For the binaries we discuss additional properties, such as wind—wind collision (WWC) effects, and the orbital parameters. With this large sample, we discuss the global properties of the WNE population, which is expected to be the most sensitive to binary evolution with respect to the influence of metallicity. To emphasize the relevance of the binary frequency test for the stellar evolution of massive stars in the LMC, we review their observational properties and provide new and meaningful evolutionary classes, which reconcile observational and theoretical definitions. Finally, we draw an overall evolutionary scheme for massive-star evolution, with respect to the three main ingredients of stellar evolution: mass, metallicity and rotation.
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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".