Tracking Martian Ion Trails Using Geometric Signal Processing and Graph-based Computational Techniques

Solar Wind Ion Analyzer (SWIA) data from the NASA Mars Atmosphere and Volatile Evolution (MAVEN) mission captures the trajectories of ion trails in the Martian ionosphere. Given the large volume of data involved, potentially running into terrabytes, it is impossible to track millions of possible individual ion trails manually, and map their trajectories across space and time. We will present geometric feature extraction techniques that can automatically identify and track the time-space evolution of pick-up ion trails in the Martian atmosphere. Our proposed method is a novel combination of several state-of-the-art geometric signal processing and optimization techniques that isolate trail morphology against background noise across SWIA energy spectra, and quantify the temporal evolution of these trails across diverse ranges of energy, differential energy flux, as well as polar and azimuthal angles. Specifically, we apply the geometric distance transform to algorithmically isolate potentially overlapping trail features within energy spectrograms, and then employ graph-based associations between isolated trails to determine connectivity between ion populations that overlap in time and space. The overall objective of our method is to connect large-scale statistical analysis with individual case studies and also connect theoretical models with potentially terrabytes of remote sensing data. We will provide results from encouraging case studies based on MAVEN SWIA data and discuss the computational benefits and implications of employing our techniques in the large scale.