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Enregistrement W4412122440 · doi:10.5194/epsc-dps2025-1168

Planetary Surface Texture Laboratory: Polarimetric Investigation of Lunar Regolith Simulants

2025· preprint· en· W4412122440 sur OpenAlexaboutno aff
A. Martin, L. O. Magaña, D. T. Blewett

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomainePhysics and Astronomy
ThématiquePlanetary Science and Exploration
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRegolithAstrobiologyPolarimetryTexture (cosmology)Remote sensingEnvironmental scienceGeologyComputer sciencePhysicsOpticsArtificial intelligenceScattering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,237
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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