We present evidence, via a large survey of 191 new spectra along with previously published spectra, of a divide in the 3 μm spectral properties of the low-albedo asteroid population. One group (“sharp types,” or STs, with band centers <3 μm) has a spectral shape consistent with carbonaceous chondrite meteorites, while the other group (“not sharp types,” or NSTs, with bands centered >3 μm) is not represented in the meteorite literature but is as abundant as the STs among large objects. Both groups are present in most low-albedo asteroid taxonomic classes, and, except in limited cases, taxonomic classifications based on 0.5–2.5 μm data alone cannot predict whether an asteroid is an ST or NST. Statistical tests show that the STs and NSTs differ in average band depth, semimajor axis, and perihelion at confidence levels ≥98% while not showing significant differences in albedo. We also show that many NSTs have a 3 μm absorption band shape like comet 67P and likely represent an important small-body composition throughout the solar system. A simple explanation for the origin of these groups is formation on opposite sides of the ammonia snow line, with the NST group accreting H2O and NH3 and the ST group only accreting H2O, with subsequent thermal and chemical evolution resulting in the minerals seen today. Such an explanation is consistent with recent dynamical modeling of planetesimal formation and delivery and suggests that much more outer solar system material was delivered to the main asteroid belt than would be thought based on the number of D-class asteroids found today.
Lunar swirls are recognized as broad, bright albedo features in various regions of the Moon. These features are often separated by dark off-swirl lanes or terminate against the dark background, such as lunar maria. Prior mapping of swirls has been done primarily by albedo contrast, which is prone to subjectivity. Closer examination of on-swirl areas shows that they are not uniform, making the boundary between on- and off-swirl difficult to map with certainty. We have applied machine learning techniques to address these issues by identifying the number of swirl units and then mapping them based on actual reflectance, or I/F data. Using LROC NAC paired stereo images that are converted to I/F reflectance at a range of incidence angles, we applied both unsupervised K-means clustering and supervised Maximum Likelihood Classification algorithms to classify and map portions of lunar swirls in Reiner Gamma and Mare Ingenii. Results show that the classification maps are a reasonable match to the representative albedos for the two study regions. A third transitionary swirl unit, termed diffuse-swirl, is present in both the maps and the cumulative distribution plots of the reflectance values. Overall, we find that the use of both algorithms provides independent confirmation of both the number and location of these units and their interrelation. More importantly, the algorithms remove mapping subjectivity by using quantitative information. The data and the statistics generated from the maps also have value in future studies by placing limits for categorizing swirl units in different regions on the Moon.
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