The role of self-heating and roughness in micro cold trap stability: implications for lunar poles
Bibliographic record
Abstract
ABSTRACT Using the Moon as a case study, we explore the effects of surface roughness at a small scale on the temperature of airless planetary bodies. Roughness can significantly influence temperature through a phenomenon known as ‘self-heating,’ in which indirect light plays a crucial role. Solar radiation can be locally reflected and scattered, causing this indirect light to reach areas of the surface that are typically not directly illuminated. This indirect contribution becomes particularly important on very rough surfaces (where self-heating occurs on at millimetre scale) or in concave geometries, such as deep craters (where self-heating occurs on a metre or tenth of metres scale). Indirect light can locally increase the temperature by several degrees, affecting the stability of likely ices. Our numerical model considers both vertical and lateral heat exchange. We analyse different rough surfaces representing ideal lunar polar sites, providing surface temperature maps, highlighting the contribution of self-heating and estimating the lifetime of potential volatiles on these surfaces. Moreover, this work could offer the thermophysical conditions that can be integrated in a Lagrangian code (such as a smoothed particle hydrodynamics code) to characterize the volatile emission and the formation of a transient exosphere. Our results suggest that in some areas, even if the self-heating can contribute to the surface temperature, water ice can remain stable in small cold traps that are only fractions of a metre in size.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".