NASA's Machine Learning to Enhance Mars Rover's Search for Life

NASA's Goddard Space Flight Center is developing a machine learning algorithm to assist in analyzing data from the upcoming ExoMars mission's Rosalind Franklin Rover, scheduled for launch no earlier than 2028. This technology aims to expedite the identification of organic compounds on Mars, allowing scientists to make quicker decisions regarding the rover's exploration strategies. The algorithm will first be tested with data from the Mars Organic Molecule Analyzer (MOMA), a key instrument on the Rosalind Franklin Rover. By rapidly filtering data, it can highlight samples that may indicate the presence of life, enabling targeted analysis and further sample collection. The rover's ability to drill up to 6.6 feet (2 meters) below the Martian surface is expected to enhance the chances of discovering preserved organic materials, potentially revealing signs of past life. The long-term vision includes achieving 'science autonomy', where the mass spectrometer could autonomously analyze data and make operational decisions, significantly boosting the efficiency of future space missions. This advancement could be pivotal for exploring distant celestial bodies, such as the moons of Saturn and Jupiter.