Kepler Eclipsing Binary Stars. VI. Identification of Eclipsing Binaries in the K2 Campaign 0 Data-set
Bibliographic record
Abstract
The original {\it Kepler} mission observed and characterized over 2400 eclipsing binaries in addition to its prolific exoplanet detections. Despite the mechanical malfunction and subsequent non-recovery of two reaction wheels used to stabilize the instrument, the {\it Kepler} satellite continues collecting data in its repurposed {\it K2} mission surveying a series of fields along the ecliptic plane. Here we present an analysis of the first full baseline {\it K2} data release: the Campaign 0 data-set. In the 7761 light curves, we have identified a total of 207 eclipsing binaries. Of these, 97 are new discoveries that were not previously identified. Our pixel-level analysis of these objects has also resulted in identification of several false positives (observed targets contaminated by neighboring eclipsing binaries), as well as the serendipitous discovery of two short period exoplanet candidates. We provide catalog cross-matched source identifications, orbital periods, morphologies and ephemerides for these eclipsing systems. We also describe the incorporation of the K2 sample into the Kepler Eclipsing Binary Catalog\footnote{\url{keplerebs.villanova.edu/k2}}, present spectroscopic follow-up observations for a limited selection of nine systems, and discuss prospects for upcoming {\it K2} campaigns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.011 |
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".