Simulating the outer layers of Procyon A: a comparison with the Sun
Bibliographic record
Abstract
We compare a new 3D radiative hydrodynamical simulation of the surface layers of Procyon A to a similar 3D simulation of the surface layers of the Sun. Both simulations include realistic input physics and are performed using the same numerical techniques and computer codes. Convection in the surface layers of Procyon A is very different from the Sun. Compared with the Sun, the atmospheric structure and convective flow in Procyon A exhibit the following characteristics. The highly superadiabatic transition layer (SAL) is located at a much shallower optical depth; it is in a dynamically active region and its outer region is sometimes located in the optically thin atmosphere. The outer region of the SAL moves from an optically thin region to a thick region and back again over a time of 20–30 min. This motion, which is driven by the granulation, takes place in a time approximately half the turnover time of the largest granules. The peak rms velocity in the vertical direction is much larger in Procyon A. The main reason for the radically different radiative-convective behaviour in Procyon A compared with the Sun is the role played by turbulent eddies in determining the overall flow/thermal structure. The turbulent pressure and turbulent kinetic energy can exceed 50 per cent of the local gas pressure (compared with about 10–20 per cent in the Sun). In such regions, the mixing lengthy theory is a poor approximation. The Procyon A simulation thus reveals two distinct time-scales: the autocorrelation time of the vertical velocity and the characteristic time-scale of the SAL, which is tied to granulation. Just below the surface, the autocorrelation decay time is about 5 min in Procyon A and the SAL motion time-scale is 20–30 min. In the simulations, the peak value of the superadiabaticity varies between 0.5 and 3. When the SAL penetrates the optically thin region, there are efficient radiative losses and the peak of the SAL is low. We speculate that these losses damp out the relative amplitudes in luminosity (temperature fluctuations) compared with velocity (Doppler). Although this will not affect the frequencies of the peaks in the power spectrum, it will probably lower the average amplitude of the peaks relative to the noise background.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".