P004-0015
Towards Science Autonomy: Applying Machine Learning Methods to ExoMars Mission’s Search for Life

Monday, 7 December 2020
Poster
Eric Lyness, NASA Goddard Space Flight Center, Washington, DC, United States, Victoria DA Poian, Southeastern Research Universities Associates, Greenbelt, MD, United States, Melissa G Trainer, NASA Goddard Space Flight Center, Greenbelt, MD, United States, William B Brinckerhoff, NASA, Greenbelt, MD, United States and Ryan Danell, Danell Consulting, Winterville, NC, United States
Abstract:
The majority of planetary missions return only one thing: data. The volume of data returned from distant planets is typically minuscule compared to Earth-based investigations, with transmissible volume decreasing further as we move to distant solar system missions. Meanwhile, the data produced by planetary science instruments continue to grow along with mission ambitions. We envision instruments that analyze science data onboard, such that they can adjust and tune automatically, select the next operations to be run without requiring ground-in-the-loop, and transmit home only the most interesting or time-critical data.

We present a first step toward this vision: a machine learning (ML) approach for analyzing science data from the Mars Organic Molecule Analyzer (MOMA) instrument, which will land on Mars within the ExoMars rover Rosalind Franklin in 2023. MOMA is a dual-source (laser desorption and gas chromatograph) mass spectrometer that will search for past or present life on the Martian surface and subsurface through analysis of soil samples. We use data collected from the MOMA flight-like engineering model to develop mass-spectrometry-focused machine learning techniques. We first apply unsupervised algorithms in order to cluster input data based on inherent patterns and to separate the bulk data into clusters. Then, optimized supervised classification algorithms designed for MOMA’s scientific goals provide information to the scientists about the likely content of the sample. This will help the scientists with their analysis of the sample and decision-making process regarding subsequent operations.

We used MOMA data to develop initial machine learning algorithms and strategies as a proof of concept to design software to support intelligent operations of more autonomous systems for future exploratory missions. This data characterization and categorization is the first step of a longer-term objective to enable the spacecraft and instruments themselves to make real-time adjustments during operations, thus optimizing the potentially complex search for life in our solar system and beyond.

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