Presentation #409.06 in the session “Asteroids: NEOs Physical Properties 1”.
Previous work indicated that a correlation may exist between radar-derived properties (e.g. circular polarization ratio, CPR) and visible IR taxonomic classes of near-Earth Asteroids . Such a relationship may indicate distinct, near-surface material properties that are dependent on taxonomic class, different near-surface evolutions, or other factors such as geometric properties of the near-surface scatterers. Here, we investigate to what extent radar scattering properties can be used as constraints to the spectro-photometric taxonomic class determination, using Arecibo S-band (12 cm, 2380 MHz) planetary radar observations of 299 NEAs as our sample. For each object observed at Arecibo from 2008-2017, disk-integrated radar properties are derived using radar backscatter from continuous wave experiments. Additionally, we collected compositional information for each object as derived through their visible-infrared Tholen & Bus-DeMeo taxonomy. For the purposes of this study, we focus on S-, C-, V- and X-type. We find that, to a high degree of confidence, CPR can be used as a diagnostic tool to identify E-type asteroids, whereby E-types have CPR > 0.75. Because E-type asteroids are part of the X complex, which also includes M and P types, CPR can be used to help resolve this group into its sub-taxonomies. Although the other taxonomic classes cannot be confidently identified by their radar properties, results indicate they can be grouped into three clusters, whereby typically M and P types have CPR < 0.3, S- and C-types have CPR within 0.2 and 0.4, and V types have CPR within 0.4 and 0.7. Therefore, taxonomic classes, in a statistical sense, are associated with CPR. Such a relationship may indicate distinct, near-surface material properties, which may be potentially derived from differences in their near-surface evolutions. Note that these derived relationships are significant only over the studied parameter space (i.e., for the range of asteroid sizes studied). Additionally, the dataset may be biased by sample size and biases in observations. Further characterization of these relationships requires modeling efforts and laboratory work in radar scattering processes.
Benner et al. (2008) Icarus 198, 294–304.