Solar FLAG★ hare and hounds: on the extraction of rotational p-mode splittings from seismic, Sun-as-a-star data
Bibliographic record
Abstract
We report on results from the first solar Fitting at Low-Angular degree Group (solar FLAG) hare-and-hounds exercise. The group is concerned with the development of methods for extracting the parameters of low-l solar p-mode data (‘peak bagging’), collected by Sun-as-a-star observations. Accurate and precise estimation of the fundamental parameters of the p modes is a vital pre-requisite of all subsequent studies. Nine members of the FLAG (the ‘hounds’) fitted an artificial 3456-d data set. The data set was made by the ‘hare’ (WJC) to simulate full-disc Doppler velocity observations of the Sun. The rotational frequency splittings of the l= 1, 2 and 3 modes were the first parameter estimates chosen for scrutiny. Significant differences were uncovered at l= 2 and 3 between the fitted splittings of the hounds. Evidence is presented that suggests this unwanted bias had its origins in several effects. The most important came from the different way in which the hounds modelled the visibility ratio of the different rotationally split components. Our results suggest that accurate modelling of the ratios is vital to avoid the introduction of significant bias in the estimated splittings. This is of importance not only for studies of the Sun, but also of the solar analogues that will be targets for asteroseismic 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".