DYNAMICAL EVOLUTION OF VISCOUS DISKS AROUND BE STARS. II. POLARIMETRY
Bibliographic record
Abstract
Be stars exhibit variability for a great number of observables.Putting the pieces of the disk dynamics together is not an easy task and requires arduous modeling before achieving a good fit to the observational data.In order to guide the modeling process and make it more efficient, it is very instructive to investigate reference dynamical cases.This paper focuses on continuum polarimetric quantities and is the second of a series that aims to demonstrate the capacity of deriving the dynamical history and fundamental parameters of a classical Be star through follow-up of various observables.After a detailed study of the different opacities at play in the formation of polarized spectra, we investigate predictions of polarimetric observables in the continuum for different dynamical scenarios.Our models are based on a coupling of a hydrodynamic viscous decretion simulations in a disk and a three-dimensional non-LTE radiative transfer code.Through introduction of the polarization color diagram (PCD), we show that certain combinations of polarimetric observables exhibit features that are characteristic of a mass-loss history.This diagram also enables estimates of fundamental parameters such as the inclination angle, disk density scale, and the α viscous diffusion parameter.We present the PCD as a powerful diagnosis tool to track the dynamical phases of a Be star, such as disk build-up, dissipation, periodic, and episodic outbursts.Finally, we confront our models with observations of four Be stars that exhibited long-term polarimetric activity.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".