Single‐ and Multi‐Pass Magnetometric Subsurface Ocean Detection and Characterization in Icy Worlds Using Principal Component Analysis (PCA): Application to Triton. Issue 2 (11th February 2022) - Cochrane, C. J.; Persinger, R. R.; Vance, S. D.; Midkiff, E. L.; Castillo‐Rogez, J.; Luspay‐Kuti, A.; Liuzzo, L.; Paty, C.; Mitchell, K. L.; Prockter, L. M.
Abstract: Many moons in the solar system are thought to potentially harbor hidden oceans based on the features observed at their surfaces. However, the magnetic induction signatures measured in the vicinity of these moons provide the most compelling evidence for the presence of a subsurface ocean, specifically for the Jovian moons Europa and Callisto. Interpretation of these magnetic signatures can be challenging due to the various systematic and random sources of noise that are present in the magnetic field measurement. In this work, a novel magnetometric ocean detection methodology based on Principal Component Analysis is presented and shown to provide enhanced discrimination and geophysical characterization of ocean properties in the presence of noise and error sources. The proposed methodology is robust for a single‐encounter mission or an orbiting mission with multiple flybys. Here, it is applied to the Neptunian moon Triton as a prime example of an active, potential ocean world residing in the requisite time‐varying magnetic field environment that enables magnetic induction investigation of its interior. In addition to the usual noise sources, other confounding factors are addressed, including the presence of an intense conductive ionosphere, the small amplitude of Neptune's driving magnetic field, and the uncertainty of Neptune's magnetic phase at the time‐of‐arrival which can potentially hinder accurate ocean detection and characterization. The proposed methodology is applicable to any moon in the solar system residing in a time‐varying magnetic field environment. Plain Language Summary: The search for habitable oceans in the solar system motivates the need for advances in analytic techniques to positively determine the presence of subsurface oceans in challenging environments. The Principal Component Analysis (PCA) method described in this article is a new paradigm for processing space‐based magnetic field measurements for definitive detection and constrained characterization of subsurface oceans. Using Neptune's largest moon Triton as an example ocean world, PCA is directly applied to a three‐axis magnetic field data set and shown to be a powerful ocean classification tool for a single or multiple flybys, even in the presence of Triton's highly conducting ionosphere which can mask the magnetic response from the ocean. The method is able to reliably distinguish between the magnetic field signatures associated with the ocean‐plus‐ionosphere and ionosphere‐only model classes and can further determine key characteristics of the hidden ocean in the face of the confounding factors of a conductive ionosphere, local plasma current perturbations, spacecraft timing and position uncertainties, data outages, and various sources of instrument noise. The flexibility and extensibility afforded by the PCA‐based method enhance the existing and future capabilities for ocean detection and characterization at candidate ocean worlds throughout the solar system. Key Points: A novel sub‐surface ocean detection and characterization method has been developed based on Principal Component Analysis processing of magnetic induction data Enables differentiation between ocean‐plus‐ionosphere and ionosphere‐only induction responses in the presence of various noise sources Applied here to the compelling target of Triton, thought to possibly harbor a sub‐surface ocean beneath a highly conducting ionosphere