Effects of Airless Bodies' Regolith Structures and of the Solar Wind's Properties on the Backscattered Energetic Neutral Atoms Flux
Bibliographic record
Abstract
Abstract The surfaces of airless planetary bodies, such as the Moon or Mercury, are covered with regoliths, which interact with the solar wind. The solar protons can either be absorbed by the surface or neutralized and reflected as hydrogen energetic neutral atoms (ENAs). The ENA flux is thought to depend mostly on the structure of the upper regolith layer. By using a model combining a Monte Carlo approach to describe a solar proton's journey through the lunar surface with molecular dynamics to characterize its interactions with the regolith's grains, we highlight the surface roughness as a key parameter that influences the backscattered H ENA flux. By considering spherical silica grains, the lunar regolith's structure is described using the open-source code Large-scale Atomic/Molecular Massively Parallel Simulator (or LAMMPS), which allows a realistic description of grain-on-grain contacts. The roughness of the modeled regolith, characterized by the roughness ratio, is shown to be dictated by the surface energy and the grain-size distribution. This work shows that a rougher surface favors deeper penetration of the protons inside the regolith, which increases the number of collisions and thus decreases their reflected fraction. The angular distribution of the backscattered H ENAs is influenced by both the surface roughness and the solar zenith angle. We show that the angular distribution of the backscattered ENAs is anisotropic and is influenced by the regolith's structure, which is consistent with Chandrayaan-1 measurements. This work aims for a better understanding of the interactions ongoing at this interface and intends to look into the possibility of deducing information on the surface structure solely from ENA flux measurements. Highlighting the key structural parameters influencing the ENA backscattering will also help the development of models of surface-bounded exospheres.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".