# Science Autonomy using Machine Learning for Astrobiology

arXiv: https://arxiv.org/abs/2504.00709 · HTML: https://mlsanity.com/paper/2504.00709/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade](https://mlsanity.com/paper/2007.15129/) (2020)

## Abstract (arXiv)

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.