Spot distribution and fast surface evolution on Vega
Bibliographic record
Abstract
Abstract Spectral signatures of surface spots were recently discovered from high cadence observations of the A star Vega. We aim at constraining the surface distribution of these photospheric inhomogeneities and investigating a possible short-term evolution of the spot pattern. Using data collected over five consecutive nights, we employ the Doppler imaging method to reconstruct three different maps of the stellar surface, from three consecutive subsets of the whole time series. The surface maps display a complex distribution of dark and bright spots, covering most of the visible fraction of the stellar surface. A number of surface features are consistently recovered in all three maps, but other features seem to evolve over the time span of observations, suggesting that fast changes can affect the surface of Vega within a few days at most. The short-term evolution is observed as emergence or disappearance of individual spots, and may also show up as zonal flows, with low- and high-latitude belts rotating faster than intermediate latitudes. It is tempting to relate the surface brightness activity to the complex magnetic field topology previously reconstructed for Vega, although strictly simultaneous brightness and magnetic maps will be necessary to assess this potential link.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".