Linear Modeling of Spectra of Fine Particulate Materials: Implications for Compositional Analyses of Primitive Asteroids. Issue 3 (21st March 2022) - Lowry, Vanessa C.; Donaldson Hanna, Kerri L.; Campins, Humberto; Bowles, Neil; Hamilton, Victoria E.; Brown, Eloïse C.
Abstract: In this study, we applied a sum to one constraint weighted least squares (STO WLS) model to thermal infrared (TIR) spectra of a suite of primitive asteroid analogs spectrally and volumetrically dominated by fine particulates (<38 μm). Since coarse particulate emissivity spectra combine linearly, deriving compositions from these are fairly straightforward. Across the TIR it is not as straightforward for fine particulate emissivity spectra due to the nonlinear behavior that arises from volumetric scattering between particles when the particle size becomes comparable to the wavelength of light. Using a WLS model, mixed spectra may be deconvolved into areal percentages of each end member which we assume corresponds to their volume percentages. We used a spectral library of pure mineral spectra to model TIR spectra of a suite of physical mixtures and meteorites obtained under ambient (Earth‐like) and simulated asteroid environment (SAE) conditions collected for the OSIRIS‐REx team (Donaldson Hanna et al., 2021, https://doi.org/10.1029/2020JE006624 ). The STO WLS model underestimated the modal abundances of the dominant mineral phases by ∼25.0 ± 0.1% on average over the full spectral range and ∼22.0 ± 0.4% over a limited spectral range in all of the ambient and SAE physical mixture and meteorite spectra. Minor phases present in the mixtures and meteorites (abundances ≤5%) were typically not modeled. Also, phases not present in the mixtures were commonly selected (∼2 phases on average with an average abundance of ∼25.2 ± 1.9%) to get the best mathematical fit. Plain Language Summary: Many planetary surfaces have a layer of regolith, unconsolidated materials including dust and broken rocks, composed of fine particulate materials. Using remote sensing by spacecraft or ground‐based telescopes, light is collected in the thermal infrared region of the electromagnetic spectrum from a planetary surface. Then using a mathematical model we estimate the percentages of minerals that make up the regolith. In this study, we explore the effectiveness of a mathematical model that is typically used to interpret light signals from coarse particles to see how effective it may be in interpreting more complex light signals from fine particles. We applied this model to mixtures and meteorites volumetrically dominated by fine particles that are representative of asteroids formed at the earliest times of our Solar System. We found the model underestimated percentages of minerals that make up a large proportion of the mixtures and showed no presence of minerals that represented a small proportion. We also observed that the model indicated the presence of minerals not present in the mixtures because the model was only solving mathematical equations. Key Points: Fine particulate primitive asteroid analog spectra were modeled using weighted least squares and fine particulate spectral library Model underestimated abundances of spectrally dominant mineral phases, minor phases were not modeled, and phases not present in mixtures were selected Linear modeling does not accurately estimate abundances when modeling less than 90‐µ‐sized particle materials