A novel theoretical approach to predict the interannual variability of sulfur in Mercury’s exosphere and subsurface
Bibliographic record
Abstract
The surfaces of airless bodies are constantly weathered by ions, meteoroids, and radiation, leading to the ejection of surface atoms to form a tenuous, collisionless atmosphere around the body. In the case of Mercury, its high surface temperatures can also lead to thermal desorption (TD) of atoms. Since its discovery approximately 50 years ago, Mercury’s exosphere has been extensively observed by both ground-based and space-borne telescopes. The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft operated 4 years in orbit around Mercury and allowed for the surface composition species to be inferred, notably including sulfur (S). Sulfur was, however, never observed in Mercury’s exosphere. In this study, we use a unique theoretical approach that combines modeling methods across different dimensional scales to understand the presence of sulfur on Mercury. Using a 3D exospheric global model with a Monte-Carlo test-particles approach and accounting for species diffusion in the first meter of Mercury’s regolith, this study aims to provide the first global prediction of the interannual variability of neutral sulfur density in both Mercury’s exosphere and subsurface. Our model predicts the formation of subsurface reservoirs at different depths according to the planetary longitude, with an equatorial reservoir peak location at ∼ 21 cm and ∼ 8 cm below the surface at the hot and cold poles, respectively. Cold longitudes are also predicted to accumulate 6.7 times more sulfur than the hot longitudes. Regarding the exosphere, the larger abundance of sulfur at the cold longitudes induces a local enhancement of the exospheric density around aphelion. The calcium surface abundance is predicted to influence the sulfur adsorption location, leading to a sulfur content enhancement in the vicinity of the −90°E longitude. Our results could be beneficial for optimizing the planning and aiding the analysis and interpretation of future observations of Mercury’s exosphere by BepiColombo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".