The Nature of<i>p</i>‐Modes and Granulation in Procyon: New<i>MOST</i>Photometry and New Yale Convection Models
Bibliographic record
Abstract
We present new photometry of Procyon, obtained by MOST during a 38 day run in 2007, and frequency analyses of those data. The long time coverage and low point-to-point scatter of the light curve yield an average noise amplitude of about 1.5-2.0 ppm in the frequency range 500-1500 μHz. This is half the noise level obtained from each of the previous two Procyon campaigns by MOST in 2004 and 2005. The 2007 MOST amplitude spectrum shows some evidence for p -mode signal: excess power centered near 1000 μHz and an autocorrelation signal near 55 μHz (suggestive of a mode spacing around that frequency), both consistent with p -mode model predictions. However, we do not see regularly spaced frequencies aligned in common l -valued ridges in echelle diagrams of the most significant peaks in the spectrum unless we select modes from the spectrum using a priori assumptions. The most significant peaks in the spectrum are scattered by more than ±5 μHz about the predicted l -valued ridges, a value that is consistent with the scatter among individually identified frequencies obtained from ground-based radial velocity (RV) observations. We argue that the observed scatter is intrinsic to the star, due to short lifetimes of the modes and the dynamic structure of Procyon's thin convection zone. We compare the MOST Procyon amplitude and power density spectra with preliminary results of three-dimensional numerical models of convection by the Yale group. These models show that, unlike in the Sun, Procyon's granulation signal in luminosity has a peak coinciding with the expected frequency region for p -modes near 1000 μHz.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".