Oscillation mode frequencies of 61 main-sequence and subgiant stars observed by
Bibliographic record
Abstract
Context. Solar-like oscillations have been observed by Kepler and CoRoT in several solar-type stars, thereby providing a way to probe the stars using asteroseismology Aims. We provide the mode frequencies of the oscillations of various stars required to perform a comparison with those obtained from stellar modelling. Methods. We used a time series of nine months of data for each star. The 61 stars observed were categorised in three groups: simple, F-like, and mixed-mode. The simple group includes stars for which the identification of the mode degree is obvious. The F-like group includes stars for which the identification of the degree is ambiguous. The mixed-mode group includes evolved stars for which the modes do not follow the asymptotic relation of low-degree frequencies. Following this categorisation, the power spectra of the 61 main-sequence and subgiant stars were analysed using both maximum likelihood estimators and Bayesian estimators, providing individual mode characteristics such as frequencies, linewidths, and mode heights. We developed and describe a methodology for extracting a single set of mode frequencies from multiple sets derived by different methods and individual scientists. We report on how one can assess the quality of the fitted parameters using the likelihood ratio test and the posterior probabilities. Results. We provide the mode frequencies of 61 stars (with their 1-σ error bars), as well as their associated échelle diagrams.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".