Planetary Surface Texture Laboratory: Polarimetric Investigation of Lunar Regolith Simulants
Bibliographic record
Abstract
Photometry measures the intensity of light from a source, most commonly used in optics and remote sensing, in order to analyze planetary surfaces and measure brightness, also known as reflectance. Reflectance is particularly important in lunar surface analysis because it offers valuable insights into surface composition and properties. The Planetary Surface Texture Laboratory (PSTL) is a facility at Johns Hopkins Applied Physics Laboratory that houses a goniometer system designed to improve the understanding of the polarization and photometric characterizations of planetary surface analog materials. This large arc system, (~1.5 meter radius), includes a sample stage and 2 caddies; one holds the polarimetric camera while the other holds the semi-collimated, unpolarized light. The phase angles range between 20° (at i = 40°) to 120° (at i = -60°) for this study; the camera (viewing angle) remains constant as the light source moves throughout the data collection. The reflectance of a material varies with the photometric conditions and is a function of properties such as particle size, porosity, roughness, and internal particle scattering behavior. The overall albedo and color of a surface may vary with the phase geometry (angles between the light source and the detecting optics). This is of particular importance because of the extreme viewing geometries encountered at the lunar south pole.Lunar Simulant Evaluation The availability and use of lunar regolith simulants is crucial for future lunar missions and understanding how to support a sustainable presence on the surface. When using lunar simulants, we have to keep in mind that the simulants are approximations and do not possess all the same characteristics of lunar regolith. However, understanding how lunar simulants differ spectrally from lunar regolith by observing the optical properties is important for providing crucial information about the composition, application, and formation history of the Moon. We assessed 12 different lunar regolith simulants from 5 different simulant provider companies; Colorado School of Mines (CSM), Off Planet Research, Space Resource Technologies (previously Exolith), NASA/United States Geological Survey (USGS) and Deltion, (4 based in the United States, 1 based in Canada, respectively). These simulants included representation for dust, mare, nearside and farside highland regolith. Our preliminary analysis shows that the composition of these simulants has an effect on the reflectance behavior. All of the highland simulants have a higher overall reflectance than the mare simulants. This is because the majority of minerals that make up the lunar highland simulants is largely plagioclase, which absorb little light, causing a higher reflectance. While the majority of minerals that make up the lunar mare simulants is largely olivine and pyroxene, which absorbs more light, causing a lower reflectance. Any variation in reflectance as a function of phase angle can be attributed to several factors such as albedo, composition, and physical properties including the scattering behavior of the individual particles, and porosity. We also collected measurements of samples that were prepped differently in order to show similarities to actual lunar terrains (as viewed from an Earth-based telescope or from orbit). Typically, to understand the full reflectance range of the material, our samples are prepped with a smooth surface. We also prepared a textured sample that was randomly chopped until it had the same depth as the sample holder. As expected, the contrasting degree of shadowing between a smooth and a rough surface can be seen. The textured sample has a rougher surface, therefore, has more micro-shadowing, causing the textured surface to be darker than the smooth surface. The effect is most pronounced in forward scattering conditions (i.e., large phase angles, >90°). Overall, our initial findings produced expected results, although a more detailed study is underway. In general, these simulants can provide a means for developing in-situ resource utilization technologies, lunar soil testing, extraction, construction, and astronaut trainings. We can also use this data to improve remote sensing techniques and calibrate upcoming mission instruments to refine photometric simulations. Being able to provide a framework for improved interpretations of phase and polarimetric observations of planetary surfaces would ultimately be beneficial for future planetary studies.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".