Exomoon candidates from transit timing variations: eight <i>Kepler</i> systems with TTVs explainable by photometrically unseen exomoons
Bibliographic record
Abstract
ABSTRACT If a transiting exoplanet has a moon, that moon could be detected directly from the transit it produces itself, or indirectly via the transit timing variations (TTVs) it produces in its parent planet. There is a range of parameter space where the Kepler Space Telescope is sensitive to the TTVs exomoons might produce, though the moons themselves would be too small to detect photometrically via their own transits. The Earth’s Moon, for example, produces TTVs of 2.6 min amplitude by causing our planet to move around their mutual centre of mass. This is more than Kepler’s short-cadence interval of 1 min and so nominally detectable (if transit timings can be measured with comparable accuracy), even though the Moon’s transit signature is only 7 per cent that of Earth’s, well below Kepler’s nominal photometric threshold. Here, we examine several Kepler systems, exploring the hypothesis that an exomoon could be detected solely from the TTVs it induces on its host planet. We compare this with the alternate hypothesis that the TTVs are caused by an non-transiting planet in the system. We examine 13 Kepler systems and find 8 where both hypotheses explain the observed TTVs equally well. Though no definitive exomoon detection can be claimed on this basis, the observations are nevertheless completely consistent with a dynamically stable moon small enough to fall below Kepler’s photometric threshold for transit detection, and these systems warrant further observation and analysis.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".