Science Autonomy using Machine Learning for Astrobiology

AI and ML enable rapid processing of large datasets, and offer advanced feature extraction and pattern recognition capabilities that deliver meaningful insights, enhancing human analysts' ability to identify correlations within complex, multi - variable datasets. This is especially needed for astrobiology, where m odels must distinguish complex biotic patterns fro m intricate abiotic backgrounds. As data volume outpaces the capacity for timely data analysis, AI and ML become essential for data processing. They could also prove invaluable for the complex data analysis that will accompany flight instruments ' advancements. ML has been widely applied in image processing of large datasets in astrophysics and Earth observation ( e.g., crater identification [2 - 4], sample targeting [5]). Similar techniques that share methodology but are improved for onboard computational rest rictions could be leveraged for astrobiology missions to identify key features [6].