A Preliminary Seismic Analysis of 51 Pegasi: Large and Small Spacings from Standard Models
Bibliographic record
Abstract
We present a preliminary theoretical seismic study of the astronomically famous star 51 Peg. This is done by first performing a detailed analysis within the Hertzsprung-Russell diagram (HRD). Using the Yale stellar evolution code (YREC), a grid of stellar evolutionary tracks has been constructed for the masses 1.00, 1.05, and 1.10 M ☉ , in the metallicity range Z = 0.024-0.044, and for values of the Galactic helium enrichment ratio (Δ Y /Δ Z ) in the range 0-2.5. Along these evolutionary tracks, we select 75 stellar model candidates that fall within the 51 Peg observational error box in the HRD (all turn out to have masses of 1.05 and 1.10 M ☉ ). The corresponding allowable age range for these models, which depends sensitively on the parameters of the model, is relatively large, ~2.5-5.5 Gyr. For each of the 75 models, a nonradial pulsation analysis is carried out and the large- and small-frequency spacings are calculated. The results show that just measuring the large- and small-frequency spacings will greatly reduce the present uncertainties in the derived physical parameters and in the age of 51 Peg. Finally, we briefly discuss refinements in the physics of the models and in the method of analysis, which will have to be included in future models to make the best of the precise frequency determinations expected from space observations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".