Science Autonomy using Machine Learning for Astrobiology
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.
Cite
@article{arxiv.2504.00709,
title = {Science Autonomy using Machine Learning for Astrobiology},
author = {Victoria Da Poian and Bethany Theiling and Eric Lyness and David Burtt and Abigail R. Azari and Joey Pasterski and Luoth Chou and Melissa Trainer and Ryan Danell and Desmond Kaplan and Xiang Li and Lily Clough and Brett McKinney and Lukas Mandrake and Bill Diamond and Caroline Freissinet},
journal= {arXiv preprint arXiv:2504.00709},
year = {2025}
}
Comments
8 pages (expanded citations compared to 5 page submitted version for DARES white papers), a white paper for the 2025 NASA Decadal Astrobiology Research and Exploration Strategy (DARES)