A TESS Dress Rehearsal: Planetary Candidates and Variables from K2 Campaign 17
Bibliographic record
Abstract
Abstract We produce light curves for all ∼34,000 targets observed with K2 in Campaign 17 (C17), identifying planet candidates, eclipsing binaries, and other periodic variables. The forward-facing direction of the C17 field means follow-up can begin immediately now that the campaign has concluded and interesting targets have been identified. The C17 field has a large overlap with C6, so this latest campaign also offers an infrequent opportunity to study a large number of targets already observed in a previous K2 campaign. The timing of the C17 data release, shortly before science operations begin with the Transiting Exoplanet Survey Satellite (TESS), also lets us exercise some of the tools and methods developed for identification and dissemination of planet candidates from TESS. We find excellent agreement between these results and those identified using only K2-based tools. Among our planet candidates are several planet candidates with sizes <4 R ⊕ and orbiting stars with Kp ≲ 10 (indicating good RV targets of the sort TESS hopes to find) and a Jupiter-sized single-transit event around a star already hosting a 6 day planet candidate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".