用机器学习提升太空任务自主性，助力寻找外星生命迹象。
Science Autonomy using Machine Learning for Astrobiology
- 基于机器学习构建自主决策系统，实时分析太空数据。
- 可有效区分生物信号与复杂非生物背景，提升探测灵敏度。
- 适合航天科研、智能探测系统开发者参考。
近年来，人工智能（AI）特别是机器学习（ML）已成为太空任务的关键工具，实现快速数据处理、高级模式识别和深入洞察提取。在天体生物学应用中，这些模型需从复杂的非生物背景中识别出生物特征模式。将自主性通过AI和ML深度整合进太空任务是一项复杂挑战，我们相信聚焦关键领域可取得显著进展，并为克服这些障碍提供切实可行的建议。
原文摘要 · Abstract (English)
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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