Magnetically controlled mass-loss from extrasolar planets in close orbits
Bibliographic record
Abstract
We consider the role magnetic fields play in guiding and controlling mass-loss via evaporative outflows from exoplanets that experience UV irradiation. First, we present analytic results that account for planetary and stellar magnetic fields, along with mass-loss from both the star and planet. We then conduct series of numerical simulations for gas giant planets, and vary the planetary field strength, background stellar field strength, UV heating flux, and planet mass. These simulations show that the flow is magnetically controlled for moderate field strengths and even the highest UV fluxes, i.e. planetary surface fields BP ≳ 0.3 G and fluxes FUV ∼ 106 erg s−1. We thus conclude that outflows from all hot Jupiters with moderate surface fields are magnetically controlled. The inclusion of magnetic fields highly suppresses outflow from the night side of the planet. Only the magnetic field lines near the pole are open and allow outflow to occur. The fraction of open field lines depends sensitively on the strength (and geometry) of the background magnetic field from the star, along with the UV heating rate. The net effect of the magnetic field is to suppress the mass-loss rate by (approximately) an order of magnitude. Finally, some open field lines do not allow the flow to pass smoothly through the sonic point; flow along these streamlines does not reach a steady state, resulting in time-variable mass-loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".