KEPLER ECLIPSING BINARY STARS. VIII. IDENTIFICATION OF FALSE POSITIVE ECLIPSING BINARIES AND RE-EXTRACTION OF NEW LIGHT CURVES
Bibliographic record
Abstract
ABSTRACT The Kepler mission has provided unprecedented, nearly continuous photometric data of ∼200,000 objects in the ∼105 deg2 field of view (FOV) from the beginning of science operations in May of 2009 until the loss of the second reaction wheel in May of 2013. The Kepler Eclipsing Binary Catalog contains information including but not limited to ephemerides, stellar parameters, and analytical approximation fits for every known eclipsing binary system in the Kepler FOV. Using target pixel level data collected from Kepler in conjunction with the Kepler Eclipsing Binary Catalog, we identify false positives among eclipsing binaries, i.e., targets that are not eclipsing binaries themselves, but are instead contaminated by eclipsing binary sources nearby on the sky and show eclipsing binary signatures in their light curves. We present methods for identifying these false positives and for extracting new light curves for the true source of the observed binary signal. For each source, we extract three separate light curves for each quarter of available data by optimizing the signal-to-noise ratio, the relative percent eclipse depth, and the flux eclipse depth. We present 289 new eclipsing binaries in the Kepler FOV that were not targets for observation, and these have been added to the catalog. An online version of this catalog with downloadable content and visualization tools is maintained at http://keplerEBs.villanova.edu .
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".