Spectropolarimetric characterisation of exoplanet host stars in preparation of the <i>Ariel</i> mission
Bibliographic record
Abstract
Context. The accurate characterisation of the stellar magnetism of planetary host stars has been gaining momentum, especially in the context of transmission spectroscopy investigations of exoplanets. Indeed, the magnetic field regulates the amount of energetic radiation and stellar wind impinging on planets, as well as the presence of inhomogeneities on the stellar surface that hinder the precise extraction of the planetary atmospheric absorption signal. Aims. We initiated a spectropolarimetric campaign to unveil the magnetic field properties of known exoplanet hosting stars included in the current list of potential Ariel targets. In this work, we focus on HD 63433, a young solar-like star hosting two sub-Neptunes and an Earth-sized planet. These exoplanets orbit within 0.15 au from the host star and have likely experienced different atmospheric evolutionary paths. Methods. We analysed optical spectropolarimetric data collected with ESPaDOnS, HARPSpol, and Neo-Narval to compute the magnetic activity indices ($ \log R^\prime_{\rm HK} $ , Hα, and Ca II infrared triplet), measure the longitudinal magnetic field, and reconstruct the large-scale magnetic topology via Zeeman-Doppler imaging (ZDI). The magnetic field map was then employed to simulate the space environment in which the exoplanets orbit. Results. The reconstructed stellar magnetic field has an average strength of 24 G and it features a complex topology with a dominant toroidal component, in agreement with other stars of a similar spectral type and age. Our simulations of the stellar environment locate 10% of the innermost planetary orbit inside the Alfvén surface and, thus, brief magnetic connections between the planet and the star can occur. The outer planets are outside the Alfvén surface and a bow shock between the stellar wind and the planetary magnetosphere could potentially form.
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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 | 0.000 |
| 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".