Modeling the Dielectric Properties of Minerals From Crystals to Bulk Powders for Improved Interpretation of Asteroid Radar Observations
Bibliographic record
Abstract
Abstract Planetary radar has provided a growing number of data sets on the inner planets and near‐Earth and main belt asteroid populations in the solar system. Physical interpretation of radar data for inference of surface properties requires constraints on the constitutive parameters of the material making up a given surface. In this study, the complex permittivity of seven minerals as a function of frequency and porosity is measured using the coaxial transmission line method to determine the mixing equation that best describes the relationship between the real part of the complex permittivity of single mineral crystals and granular mineral powders. We find the Looyenga‐Landau‐Lifshitz and Bruggeman symmetric mixing equations to describe our experimental results with the highest accuracy. The variation in the real part of the permittivity of solid mineral crystals between different minerals is shown to depend on the grain density and the chemical composition of the minerals. These mixing relationships are incorporated into an asteroid radar model and used to calculate the porosity in the near‐surface of seven asteroids visited by robotic spacecraft using Earth‐based radar observations. The results of the asteroid radar model support the presence of significant porosity in the boulders on the surface of asteroid 101955 Bennu. This research highlights the ability of radar to measure the porosity on asteroid surfaces and provides theoretical and experimental justification for the inversion of permittivity to bulk density assumed by the asteroid radar model.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".