A SYSTEMATIC SEARCH FOR COROTATING INTERACTION REGIONS IN APPARENTLY SINGLE GALACTIC WOLF-RAYET STARS. II. A GLOBAL VIEW OF THE WIND VARIABILITY
Bibliographic record
Abstract
This study is the second part of a survey searching for large-scale spectroscopic variability in apparently single Wolf-Rayet (WR) stars. In a previous paper (Paper I), we described and characterized the spectroscopic variability level of 25 WR stars observable from the northern hemisphere and found 3 new candidates presenting large-scale wind variability, potentially originating from large-scale structures named corotating interaction regions (CIRs). In this second paper, we discuss an additional 39 stars observable from the southern hemisphere. For each star in our sample, we obtained 4–5 high-resolution spectra with a signal-to-noise ratio of ∼100 and determined its variability level using the approach described in Paper I. In total, 10 new stars are found to show large-scale spectral variability of which 7 present CIR-type changes (WR 8, WR 44, WR55, WR 58, WR 61, WR 63, WR 100). Of the remaining stars, 20 were found to show small-amplitude changes and 9 were found to show no spectral variability as far as can be concluded from the data on hand. Also, we discuss the spectroscopic variability level of all single galactic WR stars that are brighter than v ∼ 12.5, and some WR stars with 12.5 < v ⩽ 13.5, i.e., all the stars presented in our two papers and four more stars for which spectra have already been published in the literature. We find that 23/68 stars (33.8%) present large-scale variability, but only 12/54 stars (∼22.1%) are potentially of CIR type. Also, we find that 31/68 stars (45.6%) only show small-scale variability, most likely due to clumping in the wind. Finally, no spectral variability is detected based on the data on hand for 14/68 (20.6%) stars. Interestingly, the variability with the highest amplitude also has the widest mean velocity dispersion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".