| ABSTRACT: Scientists and science-fiction alike dream of exploring distant worlds that could harbor life outside of Earth. While humans cannot currently travel to the most promising candidates for life in our solar system - ocean worlds such as Europa and Enceladus - our spacecraft can. Our group has been developing a collaborative, onboard AI capability that draws not only from the expertise of human scientists, but one that can learn 'onthe-fly'. We use multiple machine learning (ML) algorithms developed for mass spectrometry as a priori models - effectively as subject matter expertise in our AI framework. The framework analyzes data onboard, and multiple ML models act as a 'science team' with different perspectives. Here I will discuss how we use ML algorithms as an onboard science team and present a simulated mission scenario to Enceladus that demonstrates our onboard AI capability during a possible life detection event. Having this capability onboard would empower missions to collect more observations and Earth-based science teams to focus on interpretation and discovery. | 
