On the Origins of Mercury's Sodium Exospheric Cold Poles Enhancement: A Multiscale Exosphere Global Model
Bibliographic record
Abstract
Abstract Mercury's exosphere is sustained by the continuous ejection of atoms from its surface, driven by solar wind, micro‐meteoroid impacts, and surface heating. Due to its 3:2 spin‐orbit resonance, some longitudes experience greater solar exposure, creating temperature variations from ∼90 to 700 K. This resonance also creates least exposed longitudes, called cold longitudes. These variations, combined with surface‐solar interactions, lead to complex exospheric dynamics. Observations from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft revealed localized enhancements in sodium column density at cold longitudes near the aphelion, where these regions rotate into the day‐side. Existing models do not explain this cold‐pole enhancement but assume a smooth, impermeable surface, neglecting the highly porous nature of Mercury's regolith. This porosity allows for subsurface diffusion of volatiles through adsorption and desorption across regolith grains, influenced by temperature and species‐specific surface binding energies. We couple a subsurface transport model with a 3D Monte Carlo‐based Exosphere Global Model to investigate whether sodium accumulated in Mercury's regolith could explain the cold pole enhancement. Results suggest that at cold longitudes, low temperatures favor sodium retention near the surface. The gradual heating induces a release of sodium from the subsurface, producing the observed localized enhancements. This mechanism reconciles MESSENGER's findings with physical processes and highlights the significance of subsurface reservoirs in volatile dynamics. We highlight the need to consider regolith structure and subsurface processes in exosphere modeling. These results improve our understanding of Mercury's volatile cycle but also offer broader insights into the behavior of surface‐bound species on airless planetary bodies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".