Revisiting the Long-period Transiting Planets from Kepler
Bibliographic record
Abstract
Abstract Currently, we have only limited means to probe the presence of planets at large orbital separations. Foreman-Mackey et al. searched for long-period transiting planets in the Kepler light curves using an automated pipeline. Here, we apply their pipeline, with minor modifications, to a larger sample and use updated stellar parameters from Gaia DR2. The latter boosts the stellar radii for most of the planet candidates found by FM16, invalidating a number of them as false positives. We identify 15 candidates, including two new ones. All have sizes from 0.3 to 1 R J, and all but two have periods from 2 to 10 yr. We report two main findings based on this sample. First, the planet occurrence rate for the above size and period ranges is planets per Sun-like star, with the frequency of cold Jupiters agreeing with that from radial velocity surveys. Planet occurrence rises with decreasing planet size, roughly describable as with , i.e., Neptune-sized planets are some four times more common than Jupiter-sized ones. Second, five out of our 15 candidates orbit stars with known transiting planets at shorter periods, including one with five inner planets. We interpret this high incidence rate to mean: (1) almost all our candidates should be genuine; (2) across a large orbital range (from ∼0.05 to a few astronomical units), mutual inclinations in these systems are at most a few degrees; and (3) large outer planets exist almost exclusively in systems with small inner planets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".