On the degree of dynamical packing in the Kepler multiplanet systems
Bibliographic record
Abstract
ABSTRACT Current planet formation theories rely on initially compact orbital configurations undergoing a (possibly extended) phase of giant impacts following the dispersal of the dissipative protoplanetary disc. The orbital architectures of observed mature exoplanet systems have likely been strongly sculpted by chaotic dynamics, instabilities, and giant impacts. One possible signature of systems continually reshaped by instabilities and mergers is their dynamical packing. Early Kepler data showed that many multiplanet systems are maximally packed – placing an additional planet between an observed pair would make the system unstable. However, this result relied on placing the inserted planet in the most optimistic configuration for stability (e.g. circular orbits). While this would be appropriate in an ordered and dissipative picture of planet formation (i.e. planets dampen into their most stable configurations), we argue that this best-case scenario for stability is rarely realized due to the strongly chaotic nature of planet formation. Consequently, the degree of dynamical packing in multiplanet systems under a realistic formation model is likely significantly higher than previously realized. We examine the full Kepler multiplanet sample through this new lens, showing that $\sim 60{{-}}95~{{\ \rm per\ cent}}$ of Kepler multiplanet systems are strongly packed and that dynamical packing increases with multiplicity. This may be a signature of dynamical sculpting or of undetected planets, showing that dynamical packing is an important metric that can be incorporated into planet formation modelling or when searching for unseen 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".