REVISED STELLAR PROPERTIES OF <i>KEPLER</i> TARGETS FOR THE QUARTER 1-16 TRANSIT DETECTION RUN
Bibliographic record
Abstract
We present revised properties for 196,468 stars observed by the NASA Kepler mission and used in the analysis of Quarter 1–16 (Q1–Q16) data to detect and characterize transiting planets. The catalog is based on a compilation of literature values for atmospheric properties (temperature, surface gravity, and metallicity) derived from different observational techniques (photometry, spectroscopy, asteroseismology, and exoplanet transits), which were then homogeneously fitted to a grid of Dartmouth stellar isochrones. We use broadband photometry and asteroseismology to characterize 11,532 Kepler targets which were previously unclassified in the Kepler Input Catalog (KIC). We report the detection of oscillations in 2762 of these targets, classifying them as giant stars and increasing the number of known oscillating giant stars observed by Kepler by ∼20% to a total of ∼15,500 stars. Typical uncertainties in derived radii and masses are ∼40% and ∼20%, respectively, for stars with photometric constraints only, and 5%–15% and ∼10% for stars based on spectroscopy and/or asteroseismology, although these uncertainties vary strongly with spectral type and luminosity class. A comparison with the Q1–Q12 catalog shows a systematic decrease in radii of M dwarfs, while radii for K dwarfs decrease or increase depending on the Q1–Q12 provenance (KIC or Yonsei–Yale isochrones). Radii of F–G dwarfs are on average unchanged, with the exception of newly identified giants. The Q1–Q16 star properties catalog is a first step toward an improved characterization of all Kepler targets to support planet-occurrence studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.004 |
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".