Disc tearing in a Be star: predicted 3D observations
Bibliographic record
Abstract
ABSTRACT We build on our previous work involving smoothed particle hydrodynamic simulations of Be stars, by using the model that exhibited disc tearing as input into the three-dimensional Monte Carlo radiative transfer code hdust to predict observables from a variety of viewing angles throughout the disc tearing process. We run one simulation at the start of each orbital period from 20 to 72 orbital periods, which covers two complete disc tearing events. The resulting trends in observables are found to be dependent on the relative position of the observer and the tearing disc. The H $\rm \alpha$ equivalent width, V magnitude, and polarization can all increase or decrease in any combination depending on the viewpoint of the observer. The H $\rm \alpha$ line profile also displays changes in strength and peak separation throughout the tearing process. We show how the outer disc of the torn system can have a large effect on the H $\rm \alpha$ line profile, and also contributes to a wavelength-dependent polarization position angle, resulting in a similar sawtooth shape to the polarization percentage. Finally, we compare our predictions to Pleione (28 Tau) where evidence has suggested that a disc tearing event has occurred in the past. We find that our tearing disc model can broadly match the trends seen in Pleione’s observables, as well as produce the two-component H $\rm \alpha$ lines observed in Pleione. This is the strongest evidence, thus far, of Pleione’s disc having indeed experienced a tearing event.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".