Simulating Spectrophotometric Fluctuations of<i>p</i>‐Mode Oscillations in Solar‐like Stars
Bibliographic record
Abstract
The solar‐like spectrophotometric variations due to nonradial oscillations were simulated using a series of synthetic spectra computed and interpolated from stellar model atmospheres, each in complete equilibrium but at varying effective temperatures. The simulations are in good agreement with observations of the Sun done by others. We have demonstrated how the p ‐mode signal is distributed as a function of wavelength between 350 and 1000 nm and more specifically how most of the signal is concentrated at shorter wavelengths. We attribute this to the high density of spectral lines at shorter wavelengths, as well as the tendency of the majority of the lines to weaken with increasing effective temperature. Our simulations are also in agreement with the observational studies that report that stronger lines show larger relative variations. We also reproduce the observed exceptions to this trend for lines such as Balmer lines and Ca ii H and K. After simulating a sample observing run, we show that it is very advantageous to take a ratio of two spectral bands, observed simultaneously, that exhibit completely different fractional changes. This is especially important for ground‐based observations that need to suppress scintillation noise and any other intensity fluctuations. Although strong absorption lines could be used for this purpose, a much better contrast can be obtained by using a wider spectral region, say between 350 and 500 nm, in conjunction with a region at much longer wavelengths, for example, in the 800 nm region. Our simple treatment of solar‐like oscillations also indicates that there is no signal loss due to lower resolution, since no single spectral line offsets the shift in the continuum at solar temperatures, as long as only the photometric signal, and not velocity shifts, is the primary interest.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".