Observational study of double superoutbursts in long-orbital-period AM CVn stars
Bibliographic record
Abstract
Abstract We present an observational study of superoutbursts in six AM CVn systems with orbital periods longer than 35 min. We focused on this range because such systems are expected to have very low mass ratios, making them particularly likely to exhibit double superoutbursts and fading-tail profiles. Using time-resolved photometry from the Variable Star Network and the American Association of Variable Star Observers campaigns, complemented by survey data from the Zwicky Transient Facility, the All-Sky Automated Survey for Supernovae, the Asteroid Terrestrial-impact Last Alert System, Gaia, and the Transiting Exoplanet Survey Satellite, we analyzed nine superoutbursts observed between 2014 and 2021. Among them, four events exhibited clear double superoutburst profiles, analogous to those seen in hydrogen-rich WZ Sge-type dwarf novae. We characterized the fading tail following the second superoutburst and found that it can be divided into three distinct phases (tails A, B, and C) with different power-law fading indices. The effective superoutburst durations were typically 7–13 d, while the dip duration is significantly different among systems. Notably, the effective durations of the second superoutbursts in the double superoutburst were consistently around 5 d. These results highlight the importance of thermal–tidal instabilities in explaining the outbursts of long-orbital-period AM CVn stars. They also suggested that the three-phase fading-tail structure may be a universal feature of the systems and further imply that in systems with short supercycles the effective viscosity during quiescence could be higher than expected from a true quiescent state.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".