Thermophysical Diversity of Young Lunar Crater Ejecta Revealed with LRO Diviner Observations
Bibliographic record
Abstract
Abstract Young (<1 Ga) craters on the Moon are known to host diverse mixtures of ejecta with varying spectral and physical properties. In this work, we examine 13 yr of bolometric surface temperature data from the Diviner Lunar Radiometer on board the Lunar Reconnaissance Orbiter over the ejecta blankets of 10 lunar craters of varying sizes (D = 5–43 km) and ages (<10 to ∼200 Ma) to study the spatial variation in their thermophysical characteristics. We find that a one-dimensional thermal model with two free parameters—the bottom-layer bulk density, ρ d , and the transition height between the surface and bottom-layer densities, H—is able to accurately fit these data over our study regions, in contrast to previous models that assumed a constant ρ d . Based on the best-fit model parameters, young crater ejecta can be divided into three classes: (1) “blocky” regions with a high abundance of boulders >1 m in diameter, (2) “clastic” ejecta with varying levels of vertical density stratification, and (3) “impact melts” with high thermal inertia materials buried under a layer of less dense material. These thermophysically derived classes correlate strongly with observed morphology in high-resolution images and polarimetric signatures in decimeter-wavelength radar, and their thermophysical properties evolve distinctly with crater age. This technique represents the first time impact melt in many forms can be quantitatively distinguished by its physical properties from other types of ejecta using remote-sensing data and could have applications in validating models of impact ejecta production and deposition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".