Investigating Kozai–Lidov oscillations and disc tearing in Be star discs
Bibliographic record
Abstract
ABSTRACT Recent simulations of Be stars in misaligned binary systems have revealed that misalignment between the disc and binary orbit can cause the disc to undergo Kozai–Lidov (KL) oscillations or disc tearing. We build on our previous suite of three-dimensional smoothed particle hydrodynamic simulations of equal-mass systems by simulating eight new misaligned Be star binary systems, with mass ratios of 0.1 and 0.5, or equal-mass systems with varying viscosity. We find the same phenomena occur as previously for mass ratios of 0.5, while the mass ratio of 0.1 does not cause KL oscillations or disc tearing for the parameters examined. With increased viscosity in our equal-mass simulations, we show that these phenomena and other oscillations are damped out and do not occur. We also briefly compare two viscosity prescriptions and find they can produce the same qualitative disc evolution. Next, we use the radiative transfer code hdust to predict observable trends of a KL oscillation, and show how the observables oscillate in sync with disc inclination and cause large changes in the polarization position angle. Our models generate highly complex line profiles, including triple-peak profiles that are known to occur in Be stars. The mapping between the SPH simulations and these triple-peak features gives us hints as to where they originate. Finally, we construct interferometric predictions of how a gap in the disc, produced by KL oscillations or disc tearing, perturbs the visibility versus baseline curve at multiple wavelengths, and can cause large changes to the differential phase profile across an emission line.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".