Transit Duration Variations in Multiplanet Systems
Bibliographic record
Abstract
Abstract A planet’s orbital orientation relative to an observer’s line of sight determines the chord length for a transiting planet, i.e., the projected distance a transiting planet travels across the stellar disk. For a given circular orbit, the chord length determines the transit duration. Changes in the orbital inclination, the longitude of ascending node, or both, can alter this chord length and thus result in transit duration variations (TDVs). Variation of the full orbital inclination vector can even lead to de-transiting or newly transiting planets for a system. We use Laplace-Lagrange secular theory to estimate the fastest nodal eigenfrequencies for over 100 short-period planetary systems. The highest eigenfrequency is an indicator of which systems should show the strongest TDVs. We further explore five cases (TRAPPIST-1, Kepler-11, K2-138, Kepler-445, and Kepler-334) using direct N-body simulations to characterize possible TDVs and to explore whether de-transiting planets might be possible for these systems. A range of initial conditions are explored, in which each realization is consistent with the observed transits. We find that tens of percent of multiplanet systems have fast enough eigenfrequencies to expect large TDVs on decade timescales. For the directly integrated cases, we find that de-transiting planets might occur on decade timescales, and TDVs of 10 minutes per decade are expected to be common.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".