Searching the Entirety of Kepler Data. II. Occurrence Rate Estimates for FGK Stars
Bibliographic record
Abstract
Abstract We present exoplanet occurrence rates estimated with approximate Bayesian computation for planets with radii between 0.5 and 16 R ⊕ and orbital periods between 0.78 and 400 days orbiting FGK dwarf stars. We base our results on an independent planet catalog compiled from our search of all ∼200,000 stars observed over the Kepler mission, with precise planetary radii supplemented by Gaia DR2-incorporated stellar radii. We take into account detection and vetting efficiency, planet radius uncertainty, and reliability against transit-like noise signals in the data. By analyzing our FGK occurrence rates as well as those computed after separating F-, G-, and K-type stars, we explore dependencies on stellar effective temperature, planet radius, and orbital period. We reveal new characteristics of the photoevaporation-driven “radius gap” between ∼1.5 and 2 R ⊕ , indicating that the bimodal distribution previously revealed for P < 100 days exists only over a much narrower range of orbital periods, above which sub-Neptunes dominate and below which super-Earths dominate. Finally, we provide several estimates of the “eta-Earth” value—the frequency of potentially habitable, rocky planets orbiting Sun-like stars. For planets with sizes 0.75–1.5 R ⊕ orbiting in a conservatively defined habitable zone (0.99–1.70 au) around G-type stars, we place an upper limit (84.1th percentile) of <0.18 planets per star.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".