Science Autonomy using Machine Learning for Astrobiology

• URL: http://arxiv.org/abs/2504.00709v1
• Date: Tue, 01 Apr 2025 12:20:18 GMT
• Title: Science Autonomy using Machine Learning for Astrobiology
• Authors: Victoria Da Poian, Bethany Theiling, Eric Lyness, David Burtt, Abigail R. Azari, Joey Pasterski, Luoth Chou, Melissa Trainer, Ryan Danell, Desmond Kaplan, Xiang Li, Lily Clough, Brett McKinney, Lukas Mandrake, Bill Diamond, Caroline Freissinet,
• Abstract summary: In recent decades, artificial intelligence (AI) and machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction.<n>These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds.
• Score: 3.305775349086079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction. These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds. Advancing the integration of autonomy through AI and ML into space missions is a complex challenge, and we believe that by focusing on key areas, we can make significant progress and offer practical recommendations for tackling these obstacles.

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