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Completed TRL 4 (started at 2, targeting 4)
Collecting mass spectrometry data from the outer planets is essential in the search for life beyond Earth, but current instrument operation concepts like the ones used on the Mars Organic Molecule Analyzer (MOMA) are not suited for the environmental challenges encountered at Jupiter and beyond. Onboard intelligence is needed for autonomy in missions to (1) optimize data collection due to both reduced lifespan from harsh radiation environments and poor-quality data links from vast distances, and (2) allow the spacecraft to make decisions without a human-in-the-loop. In this proposal, we will apply several machine learning algorithms to a real data set gathered from MOMA in order to identify a practical approach for scientific autonomy in mass spectrometry. We developed state-of-the-art machine learning algorithms to both gather knowledge from mass spectroscopy data and to act upon that knowledge.
This effort enables previously impossible missions by enabling instruments that typically require human-in-the-loop control to be used in environments where this is impossible such as the ocean worlds around Jupiter and Saturn.
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