Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Bibliographic record
Abstract
Abstract Searches for helium in the exospheres of exoplanets via the metastable near-infrared triplet have yielded 17 detections and 40 nondetections. We performed a comprehensive reanalysis of published studies to investigate the influence of stellar X-ray and extreme-ultraviolet (XUV) flux and orbital parameters on the detectability of helium in exoplanetary atmospheres. We identified a distinct “orbital sweet spot” for helium detection, 0.03 to 0.08 au from the host star, where the majority of detections occurred. This sweet spot is influenced by the stellar luminosity and planet size. Notably, a lower ratio of XUV flux to mid-UV flux is preferred for planets compared to nondetections. We also found that helium detections occur for planets around stars with effective temperatures of 4400–6500 K (i.e., spectral type K and G stars), with a sharp gap between 5400 and 6000 K, where no detections occur. We also report an upper-limit efficiency of 6% for energy-limited atmospheric escape from our analysis. Additionally, our analysis of the cumulative XUV flux versus escape velocity shows planets with helium detections above the “cosmic shoreline,” where atmospheres are not thought to be present, suggesting the shoreline needs revision. The unexpected trends revealed in our meta-analysis can contribute to a better understanding of star–planet interaction and exosphere evolution.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".