Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft

Abstract

Data analysis and machine learning methods have great potential to aid in planetary exploration. Spacecraft often operate at great distances from the Earth, and the ability to autonomously detect features of interest onboard can enable content-sensitive downlink prioritization to increase mission science return. We describe algorithms that we designed to assist in three specific scientific investigations to be conducted during flybys of Jupiter's moon Europa: the detection of thermal anomalies, compositional anomalies, and plumes of icy matter from Europa's subsurface ocean. We also share the unique constraints imposed by the onboard computing environment and several lessons learned in our collaboration with planetary scientists and mission designers.