C. J. Ahrens | Searcharxiv

arXiv2025

Kiladze Caldera: A Possible Cryovolcano on Pluto

In contrast with regional primarily methane composition, Kiladze and its surroundings exhibit a water-ice spectral signature that carries an ammoniated compound, similar to two other cryovolcanic sites on Pluto. The faulted structure of Kiladze, including shaping by numerous collapse pits and the distortion of the shape of the depression, are compatible with the surroundings in Hayabusa Terra, east of Sputnik Planitia. They are further compatible with an interpretation as a caldera formed during an era of an active cryovolcanic period that appears to be significantly more recent than the overall age of the planet's surface, possibly in the last several million years. In view of the size of the caldera and the large scale of the surrounding distribution of water ice, we suggest that Kiladze may have been a cryovolcano, in which one or more explosive events may have erupted $\sim$1000 km$_{3}$ of icy cryomagma onto the surface.

astro-ph.EP↗

arXiv2023

Pluto's Surface Mapping using Unsupervised Learning from Near-Infrared Observations of LEISA/Ralph

We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geographic 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 N${}_{2}$, CH${}_{4}$, and CO and nonvolatile H${}_{2}$O -- to connect the unit to surface composition, geology, and geographic 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 analysis using spectral indices (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 is specifically 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.

astro-ph.EP↗