Time evolution of magnetic activity cycles in young suns: The curious case of <i>κ</i> Ceti
Bibliographic record
Abstract
Context. A detailed investigation of the magnetic properties of young Sun-like stars can provide valuable information on our Sun’s magnetic past and its impact on the early Earth. Aims. We determine the properties of the moderately rotating young Sun-like star κ Ceti’s magnetic and activity cycles using 50 yr of chromospheric activity data and six epochs of spectropolarimetric observations. Methods. The chromospheric activity was determined by measuring the flux in the Ca II H and K lines. A generalised Lomb–Scargle periodogram and a wavelet decomposition were used on the chromospheric activity data to establish the associated periodicities. The vector magnetic field of the star was reconstructed using the technique of Zeeman Doppler imaging on the spectropolarimetric observations. Results. Our period analysis algorithms detect a 3.1 yr chromospheric cycle in addition to the star’s well-known ~6 yr cycle period. Although the two cycle periods have an approximate 1:2 ratio, they exhibit an unusual temporal evolution. Additionally, the spectropolarimetric data analysis shows polarity reversals of the star’s large-scale magnetic field, suggesting a ~10 yr magnetic or Hale cycle. Conclusions. The unusual evolution of the star’s chromospheric cycles and their lack of a direct correlation with the magnetic cycle establishes κ Ceti as a curious young Sun. Such complex evolution of magnetic activity could be synonymous with moderately active young Suns, which is an evolutionary path that our own Sun could have taken.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".