Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Bibliographic record
Abstract
Abstract We present a new catalog of Kepler planet candidates that prioritizes accuracy of planetary dispositions and properties over uniformity. This catalog contains 4376 transiting planet candidates, including 1791 residing within 709 multiplanet systems, and provides the best parameters available for a large sample of Kepler planet candidates. We also provide a second set of stellar and planetary properties for transiting candidates that are uniformly derived for use in occurrence rate studies. Estimates of orbital periods have been improved, but as in previous catalogs, our tabulated values for period uncertainties do not fully account for transit timing variations (TTVs). We show that many planets are likely to have TTVs with long periodicities caused by various processes, including orbital precession, and that such TTVs imply that ephemerides of Kepler planets are not as accurate on multidecadal timescales as predicted by the small formal errors (typically 1 part in 106 and rarely >10−5) in the planets’ measured mean orbital periods during the Kepler epoch. Analysis of normalized transit durations implies that eccentricities of planets are anticorrelated with the number of companion transiting planets. Our primary catalog lists all known Kepler planet candidates that orbit and transit only one star; for completeness, we also provide an abbreviated listing of the properties of the two dozen nontransiting planets that have been identified around stars that host transiting planets discovered by Kepler.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".