POLARIZATION ECLIPSE MODEL OF THE WOLF-RAYET BINARY V444 CYGNI WITH CONSTRAINS ON THE STELLAR RADII AND AN ESTIMATE OF THE WOLF-RAYET MASS-LOSS RATE
Bibliographic record
Abstract
We present an improved analytical model as well as a new set of multi-wavelength observation of the polarization eclipse of the Wolf-Rayet binary V444 Cygni (WN5+O6). Comparing the model with the observations yields an estimate of the O and Wolf-Rayet star radii as well as of the Wolf-Rayet mass-loss rate. For the O star we find R = 8.5 R⊙ and for the Wolf-Rayet star R ⊙. This values are in agreement with those derived by Cherepashchuk et al. from the detailed analysis of multiwavelength light curves. For the Wolf-Rayet mass-loss rate we obtain M = 7.5 x 10-6M⊙/yr, which is compatible with the dynamical values obtained from the rate of orbital period increase and with the value of dM/dt determined from the orbital double-wave modulation in polarization, but is at least 3 times smaller than the values derived from free-free radio fluxes and modeling of infrared spectral lines. However, no allowance has been made in calculating the mass-loss rates for inhomogeneities, for which evidence is increasing in hot star winds. If the wind of the WR star V444 Cygni is found to be clumpy, the radio/IR mass-loss rates are likely to be overestimated because of their dependency on the square of density. In such case, these values would probably have to be significantly decreased, bringing them closer to the polarization estimates, for which clumpy winds are irrelevant, providing the electron scattering remains optically thin. Full version was published in: Ap. J., 410, 342-356 (1993).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".