T.: Photometric variability in Kepler target stars. II. An overview of amplitude, periodicity, and rotation in first quarter data
Bibliographic record
Abstract
We provide an overview of stellar variability in the first quarter of data from the Kepler mission. The intent of this paper is to examine the entire sample of over 150,000 target stars for periodic behavior in their lightcurves, and relate this to stellar characteristics. These data constitute an unprecedented study of stellar variability given its great precision and complete time coverage (with a half hour cadence). Because the full Kepler pipeline is not currently suitable for a study of stellar variability of this sort, we describe our procedures for treating the “raw ” pipeline data. About half of the total sample exhibits convincing periodic variability up to two weeks, with amplitudes ranging from differential intensity changes less than 10−4 up to more than 10 percent. K and M dwarfs have a greater fraction of period behavior than G dwarfs. The giants in the sample have distinctive quasi-periodic behavior, but are not periodic in the way we define it. Not all periodicities are due to rotation, and the most significant period is not necessarily the rotation period. We discuss properties of the lightcurves, and in particular look at a sample of very clearly periodic G dwarfs. It is clear that a large number of them do vary because of rotation and starspots, but it will take further analysis to fully exploit this.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".