Lifting Transit Signals from the Kepler Noise Floor. I. Discovery of a Warm Neptune
Bibliographic record
Abstract
Abstract Light curves from the 4-year Kepler exoplanet hunting mission have been searched for transits by NASA’s Kepler team and others, but there are still important discoveries to be made. We have searched the light curves of 400 Kepler Objects of Interest (KOIs) to find transit signals down to signal-to-noise ratio (S/N) ∼ 6, which is under the limit of S/N ∼ 7.1 that has been commonly adopted as a strict threshold to distinguish between a transit candidate and false alarm. We detect four new and convincing planet candidates ranging in radius from near-Mercury-size to slightly larger than Neptune. We highlight the discovery of KOI-408.05 (period = 637 days; radius = 4.9 R ⊕; incident flux = 0.6 S ⊕), a planet candidate within its host star’s Habitable Zone. We dub this planet a “warm Neptune,” a likely volatile-rich world that deserves closer inspection. KOI-408.05 joins 21 other confirmed and candidate planets in the current Kepler sample with semimajor axes a > 1.4 au. These discoveries are significant as a demonstration that the S/N threshold for detection used by the Kepler project is open to debate.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".