Autonomous Science: Simulated Solar System Mission to Enceladus, Icy Ocean Moon of Saturn

Future NASA missions to icy ocean worlds such as Europa, Titan, or Enceladus will collect mass spectrometry (MS) data from exospheres, atmospheres, and plume volatiles to assess their geochemistry and potential for microbial life1. These remote missions face challenges related to limited bandwidth, communication, and power, highlighting the need for science autonomy2 to prioritize data for timely downlink. We extend our previous work on machine learning (ML) biosignature detection from isotope ratio mass spectrometry (IRMS) data to distributed systems missions (DSM) by incorporating the ML and autonomous data quality control and prioritization code into an onboard decision-making platform. We use simulated orbital telemetry for eight orbiters around Enceladus with a mothership to illustrate an automated biosignature and novel seawater chemistry detection with data prioritization from MS analyses of volatile CO2.