Circumbinary planets – why they are so likely to transit
Bibliographic record
Abstract
Transits on single stars are rare. The probability rarely exceeds a few per cent. Furthermore, this probability rapidly approaches zero at increasing orbital period. Therefore, transit surveys have been predominantly limited to the inner parts of exoplanetary systems. Here, we demonstrate how circumbinary planets allow us to beat these unfavourable odds. By incorporating the geometry and the three-body dynamics of circumbinary systems, we analytically derive the probability of transitability, a configuration where the binary and planet orbits overlap on the sky. We later show that this is equivalent to the transit probability, but at an unspecified point in time. This probability, at its minimum, is always higher than for single star cases. In addition, it is an increasing function with mutual inclination. By applying our analytical development to eclipsing binaries, we deduce that transits are highly probable, and in some case guaranteed. For example, a circumbinary planet revolving at 1 au around a 0.3 au eclipsing binary is certain to eventually transit – a 100 per cent probability – if its mutual inclination is greater than 0| $_{.}^{\circ}$|6. We show that the transit probability is generally only a weak function of the planet's orbital period; circumbinary planets may be used as practical tools for probing the outer regions of exoplanetary systems to search for and detect warm to cold transiting planets.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".