Pluto's surface mapping using unsupervised learning from nearinfrared observations of LEISA/Ralph

by Emran, A.; Dalle Ore, C. M.; Ahrens, C. J.; Khan, M. K. H.; Chevrier, V. F.; Cruikshank, D. P.

We map the surface of Pluto using an unsupervised machine learning technique using the nearinfrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geog. distribution of the surface units across the dwarf planet. We also present the likelihood of each surface unit at the image pixel level. Average I/F spectra of each unit were analyzed - in terms of the position and strengths of absorption bands of abundant volatiles such as N2, CH4, and CO and nonvolatile H2O - to connect the unit to surface composition, geol., and geog. location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles - consistent with the existing literature. However, previous mapping efforts were based primarily on compositional anal. using spectral indexes (indicators) or implementation of complex radiative transfer models, which need (prior) expert knowledge, label data, or optical constants of representative endmembers. We prove that an application of unsupervised learning in this instance renders a satisfactory result in mapping the spatial distribution of ice compositions without any prior information or label data. Thus, such an application isspecifically advantageous for a planetary surface mapping when label data are poorly constrained or completely unknown, because an understanding of surface material distribution is vital for volatile transport modeling at the planetary scale. We emphasize that the unsupervised learning used in this study has wide applicability and can be expanded to other planetary bodies of the Solar System for mapping surface material distribution.

Journal, e-Print Archive, Astrophysics
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