An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning (ML) and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires community-wide efforts. We summarize recent progress in ML applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

中文翻译：

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用于观测宇宙学的机器学习

计划在未来十年内开展一系列使用地面和太空望远镜的大型观测计划。即将进行的大范围天空调查预计将提供超过 EB 的数据量。处理大量的多重天文数据在技术上具有挑战性，迫切需要基于机器学习 (ML) 和人工智能的全自动化技术。最大化大数据的科学回报需要全社会的努力。我们总结了观测宇宙学中 ML 应用的最新进展。我们还解决了数据处理和统计分析所需的高性能计算中的关键问题。