The exoplanetary magnetosphere extension in Sun-like stars based on the solar wind–solar UV relation
Bibliographic record
Abstract
ABSTRACT The Earth’s magnetosphere extension is controlled by the solar activity level via solar wind properties. Understanding such a relation in the Solar system is important for predicting the condition of exoplanetary magnetospheres near Sun-like stars. We use measurements of a chromospheric proxy, the Ca ii K index, and solar wind OMNI parameters to connect the solar activity variations, on decennial time-scales, to the solar wind properties. The data span the period 1965–2021, which almost entirely covers the last five solar cycles. Using both cross-correlation and mutual information analysis, we find a 3.2-yr lag of the solar wind speed with respect to the Ca ii K index. Analogously, a 3.6-yr lag is found once we consider the dynamic pressure. A correlation between the solar wind dynamic pressure and the solar ultraviolet emission is found and used to derive the Earth’s magnetopause standoff distance. Moreover, the advantage of using a chromospheric proxy, such as the Ca ii K index, creates the possibility to extend the relation found for the Sun to Sun-like stars, by linking stellar variability to stellar wind properties. The model is applied to a sample of Sun-like stars as a case study, where we assume the presence of an Earth-like exoplanet at 1 au. Finally, we compare our results with previous estimates of the magnetosphere extension for the same set of Sun-like stars.
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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".