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A machine learning approach for analyzing data from the Mars Organic Molecule Analyzer (MOMA) instrument has been developed in order to improve the accuracy and efficiency of this analysis and serves as a case study for the use of machine learning tools for space missions. MOMA is part of the science payload aboard the ExoMars rover, Rosalind Franklin, currently planned to land on Mars in 2023. Most NASA robotic space missions return only one thing: data. Remote planetary missions continue to produce more data as mission ambitions and instrument capabilities grow, yet the investigations are still limited by available bandwidth to transmit data back to Earth. To maximize the value of each bit, instruments need to be highly selective about which data are prioritized for return to Earth, as compression and transmission of the full data volume is often not feasible. The fundamental goal is to enable the concept of science autonomy, where instruments perform selected onboard science data analyses and then act upon those analyses through self-adjustment and tuning of instrument parameters. In this paper, we discuss the motivations, as well as related work on the use of machine learning for space missions. We also present a first step toward this vision of science autonomy for space science missions. This proof-of-concept exercise for the MOMA instrument aims to develop tools, used on Earth, to support Martian operations of the ExoMars mission. We also discuss the challenges and limitations of this implementation, as well as lessons learned and approaches that could be used for future space science missions.

For the past fifty years of space exploration, mass spectrometry has provided unique chemical and physical insights on the characteristics of other planetary bodies in the Solar System. A variety of mass spectrometer types, including magnetic sector, quadrupole, time-of-flight, and ion trap, have and will continue to deepen our understanding of the formation and evolution of exploration targets like the surfaces and atmospheres of planets and their moons. An important impetus for the continuing exploration of Mars, Europa, Enceladus, Titan, and Venus involves assessing the habitability of solar system bodies and, ultimately, the search for life—a monumental effort that can be advanced by mass spectrometry. Modern flight-capable mass spectrometers, in combination with various sample processing, separation, and ionization techniques enable sensitive detection of chemical biosignatures. While our canonical knowledge of biosignatures is rooted in Terran-based examples, agnostic approaches in astrobiology can cast a wider net, to search for signs of life that may not be based on Terran-like biochemistry. Here, we delve into the search for extraterrestrial chemical and morphological biosignatures and examine several possible approaches to agnostic life detection using mass spectrometry. We discuss how future missions can help ensure that our search strategies are inclusive of unfamiliar life forms.