The At. Astra Research Group: Using Atomic Modelling to Better Explain the Surface-Exosphere Connection on Airless Bodies
Bibliographic record
Abstract
For nearly 40 years, planetary science studies of the exospheres of Mercury, the Moon, and other airless bodies have been hindered because of uncertainties in our understanding of the surface processes influencing exosphere formation. The surfaces of these airless bodies can be subjected to several different emission processes including solar wind induced sputtering, photon stimulated desorption, and micrometeorite impact vaporization. However, the relative contributions of these various processes to the body’s exosphere remains contested for many observed elemental species. To obtain a true understanding of the surface-exosphere connection of airless bodies we must first improve our understanding of the interplay of these different processes and how they are affected by the specific characteristics of the surface. However, many of these key emission processes are occurring on the atomic scale, meaning global exosphere models often require atomically derived parameters as inputs. These inputs are difficult to obtain experimentally, and are therefore typically derived via fitting, often overlooking important complexities that can significantly affect predicted results. Further complicating this picture is the fact that some proportion of the ejected atoms leave the surface at energies lower than the escape energy of the body and thus return to the surface. A portion of these atoms can then be reaccommodated on the surface at an energy and composition unique from the mineral bulk. However, current global exosphere models are unable to consider the effects of adsorbed atoms nor the contribution of emission from the newly formed adsorbed layers.Here, we will discuss how molecular dynamics (MD) modelling on the atomic scale can be a critical tool to provide physically realizable and surface-specific input parameters for global exosphere models. We will discuss a series of our previous studies from our group based out of Memorial University that have used MD modelling to study key planetary science processes on the atomic scale. This will include MD models of sputtering, diffusion, surface adsorption, surface free energy, and micro meteorite impacts. For each study we will discuss key unknowns introducing uncertainties into global models. We will then apply atomic modelling to study these processes on the atomic scale, better understanding the underlying physics.Finally, we will conclude with a discussion on new areas that we can apply these approaches to new research areas including icy bodies and exoplanets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".