Science Autonomy Using Machine Learning For Astrobiology – A White Paper For 2025 NASA DARES
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.
Excerpt
1) Motivation for Science Autonomy
In the last few decades, artificial intelligence (AI) including machine learning (ML) have become essential for data analysis in space missions [1]. 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 models must distinguish complex biotic patterns from 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 restrictions could be leveraged for astrobiology missions to identify key features [6]. This paper, primarily addressing the RFI’s Topic 2 “Emerging Themes and Technologies”, focuses on using onboard intelligence (‘science autonomy’) for mass spectrometry (MS) data analysis, a powerful chemical analysis technique with high life-detection potential [7, 8]. For more details on MS for astrobiology, see Pasterski et al. DARES submission.
As space missions venture to more distant planetary bodies, they face critical challenges such as fundamental communication limits (light travel time), mission design challenges (low bandwidth) and limited power/storage resources, further strained by the increasing data volume from advanced instruments.
Missions traveling far from Earth (e.g., Dragonfly, Europa Clipper) must operate under strict data transmission constraints, limiting data availability for science analysis. Continued investment in data return facilities (e.g., Deep Space Network upgrades) and other technologies to enhance data return are required [1].
While infrastructure investments help to mitigate communication bottlenecks and maximize science return, AI and ML enable capabilities like onboard autonomy and ML-driven analysis beyond traditional infrastructures.
Our long-term vision for space missions involves in situ analysis, where spacecraft analyze data in real-time, make autonomous decisions, and prioritize scientific goals without relying solely on Earth-based instructions.
Communication of explainable decisions by autonomous agents will remain crucial for accountability and feedback. While AI and ML tools have helped to mitigate deep-space missions communication latency, advancements in capability and the growing complexity of science instruments require commensurate improvements to our current ML techniques.
These tools can enhance mission efficiency by optimizing data transmission, enabling opportunistic science (e.g., Enceladus plumes [6]), detecting anomalies, and optimizing resources like energy allocation and scheduling. Section 2 showcases ML functionality for backwardfacing applications on already-collected data, and Section 3 explores forward-facing applications to enhance and enable future space missions.
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)
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
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2504.00709 [astro-ph.IM](or arXiv:2504.00709v1 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2504.00709
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Submission history
From: Victoria Da Poian
[v1] Tue, 1 Apr 2025 12:20:18 UTC (106 KB)
https://arxiv.org/abs/2504.00709
Astrobiology,
