High‐Resolution Nighttime Temperature and Rock Abundance Mapping of the Moon Using the Diviner Lunar Radiometer Experiment With a Model for Topographic Removal
Bibliographic record
Abstract
Abstract The Diviner Lunar Radiometer Experiment on the Lunar Reconnaissance Orbiter (LRO) has been mapping the surface temperatures of the Moon since 5 July 2009. Diviner has since collected over 500 billion radiometric measurements with excellent spatial and local time coverage. However, the most recently published high‐resolution Diviner global maps only use data collected from 2009 to 2016. In this work, we compile ∼13 years of Diviner data to produce improved global maps of nighttime brightness temperature, bolometric temperature, regolith temperature, and rock abundance (RA). Errors in Diviner's pointing have been corrected and past effective field of view modeling has been optimized to improve data georeferencing without spatial interpolation. We estimate an effective resolution of ∼330 m longitudinally and ∼700 m latitudinally at the equator, which corresponds to an improvement of ∼3.5× longitudinally and ∼1.3× latitudinally. In addition, we develop a thermal model that accounts for indirect scattering and emission from surrounding topography. The resulting temperature anomaly maps better highlight variations in temperature caused by thermophysical properties by removing most topographic effects. These improvements allow for the identification of smaller and fainter thermal features than was previously possible. The improved effective resolution of Diviner maps allows for excellent spatial correlation with other high‐resolution data sets. To demonstrate this, we compare Diviner RA to a manual survey of boulders in the Apollo 17 landing site region. We show that Diviner RA correlates well with the areal fraction of rocks larger than ∼1–2 m in diameter visible in LRO Camera imagery.
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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 | 0.001 |
| 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".