¹R.Brunetto,¹C.Lantz,²T.Nakamura,¹D.Baklouti,¹T.Le Pivert-Jolivet,²S.Kobayashi,³F.Borondics
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2020.113722]
¹Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France
²Division of Earth and Planetary Materials Science, Graduate School of Science, Tohoku University, Japan
³SOLEIL Synchrotron, Gif-sur-Yvette, France
Copyright Elsevier

Solar wind ion irradiation continuously modifies the optical properties of unprotected surfaces of airless bodies in the Solar System. This alteration induces significant biases in the interpretation of the spectral data obtained through remote sensing, and it impedes a correct estimation of the composition of the sub-surface pristine materials. However, as the alteration of the surface is a function of time, an in-depth understanding of the phenomenon may provide an original way to estimate the weathering age of a surface. Laboratory experiments show that mid- and far-IR bands are very sensitive to space weathering, as they are significantly modified upon irradiation. These bands can thus constitute a reliable proxy of the time-bound effects of irradiation on an object. We show that the detection of irradiation effects is within the reach of IR spectral resolution of the OSIRIS-REx mission and of the future James Webb Space Telescope. Our results provide a possible evidence for space weathering effects in the IR spectrum of asteroid 101955 Bennu measured by OTES/OSIRIS-REx.

¹Gayantha R.L. Kodikara,²Lindsay J.McHenry
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2020.113719]
¹Department of Geosciences, University of Wisconsin-Milwaukee, 3209 N. Maryland Avenue, Milwaukee, WI 53211, USA
²Department of Geosciences, University of Wisconsin-Milwaukee, 3209 N. Maryland Avenue, Milwaukee, WI 53211, USA
Copyright Elsevier

We examine the ability of machine learning techniques to determine the physical and mineralogical properties of lunar soil using reflectance spectra. We use the Lunar Soil Characterization Consortium (LSCC) dataset to train and asses the predictive power of classification models based on their type (Mare soil and Highland soil), particle size, maturity, and the dominant type of pyroxene (High-Ca and Low-Ca). Nine ML algorithms including linear methods, non-linear methods, and rule-based methods (three from each) were selected, representing a range of characteristics such as simplicity, flexibility, computational complexity, and interpretability along with their ability to handle different types of data. Fifteen spectral parameters were initially introduced to the models as input features and a maximum of four features was selected as the best feature combinations to classify lunar soils based on their types, particle size, maturity, and the type of pyroxene. The Support Vector Machine with radial basis function (svmRadial) and the penalized logistic regression model (glmnet) performed well for all target variables with high accuracies. Band depths and Integrated band depths at 1 μm, 1.25 μm and 2 μm, band position of the 1 μm band, along with four band ratios (band tilt, band strength, band curvature and olivine/pyroxene) are important features for classifying soil type, grain size, maturity, and type of pyroxene from reflectance spectra. This study shows that proper preprocessing and feature engineering techniques are crucial for high performance of the predictive models.

¹L.C.Cheek,¹J.M.Sunshine
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2020.113718]
¹Department of Astronomy, University of Maryland, College Park, MD 20742-2421, USA
Copyright Elsevier

A major goal of the Dawn mission to Vesta was to test and refine current models of the asteroid’s formation by characterizing the distribution of mineral components on its surface. Detection of the mineral olivine by the Visible and Infrared Mapping spectrometer (VIR) and the Framing Camera (FC) onboard Dawn was a key milestone in this effort, and was expected to help resolve a debate regarding the dominant mode of Vesta’s petrologic evolution (i.e., magma ocean vs. serial magmatism). However, the subtleties of the olivine spectral signature combined with the small size of individual olivine-rich exposures (tens of meters) prohibits detailed mapping of this petrologically significant mineral component when the data from either instrument are evaluated independently. As a result, the particular role of these olivine exposures in Vesta’s geologic history remains largely unresolved.
Here, we fully characterize the type locality of Vesta’s olivine-rich materials, the northern hemisphere craters Bellicia and Arruntia, using a novel data fusion approach for linking the VIR and FC datasets. Specifically, we have leveraged a spectral mixture analysis framework to create a new dataset that maps, or “extrapolates”, full resolution spectra, derived from VIR data, onto the higher spatial resolution of FC. When used as an exploratory tool in conjunction with the original FC and VIR data, the new “extrapolated” dataset reveals important new details about the distribution of olivine around Bellicia and Arruntia craters.
There are clear compositional distinctions between the exposures at the two craters, with a much stronger olivine component in the walls of Bellicia than Arruntia. The proximal Arruntia ejecta appear more consistent with high proportions of an evolved high‑calcium pyroxene (as in eucrites) than with an enhanced olivine component. Examples of diogenite-rich howardite also occur in the region. Further, we leverage Arruntia’s role as the freshest large crater in the northern hemisphere, and thus a rare window into the Vesta’s northern hemispheric crust, to suggest an overall stratigraphy for the region: a thin veneer of howardite overlies a sequence of diogenite-rich (near surface), olivine-rich (intermediate), and eucrite-rich materials (at depth). The manner of exposure of these various components in a marked stratification point to a plutonic model for emplacing these mineralogically unique components in the near surface of Vesta.