发表机构
University of the Western Cape; South African Radio Astronomy Observatory（西开普大学; 南非射电天文观测站）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探讨机器学习能否自动化科学发现，介绍主动异常检测框架Astronomaly及其发现，并强调人机交互在挖掘海量天文数据中的关键作用。
AI 中文摘要
下一代望远镜，如平方千米阵和维拉·C·鲁宾天文台，将产生海量数据，其规模之大使得传统分析技术难以应对。机器学习在处理大规模数据量和自动化传统上由人类科学家完成的任务方面已被证明具有不可估量的价值。问题是，机器学习能否也实现科学发现的自动化？该领域的最新进展已使这成为可能，但前提是必须通过主动学习与人类专家的输入相结合。Astronomaly是一个公开可用的框架，用于天文学中的主动异常检测。它学习向用户推荐感兴趣的源，使科学家能够在大型数据集中快速发现罕见和新类别的天体。在本文中，我们回顾了使用Astronomaly做出的一些发现，讨论了最新的基于深度学习的方法，并探讨了网络-人类界面在揭示隐藏在大型天文数据集中的宇宙奥秘方面的关键作用。
英文摘要
The next generation of telescopes such as the Square Kilometre Array and the Vera C. Rubin Observatory will produce enormous quantities of data, too large for traditional analysis techniques. Machine learning has proven invaluable in handling massive data volumes and automating tasks traditionally done by human scientists. The question is, can machine learning also automate scientific discovery? Recent advances in the field have made this feasible but only when combined with expert human input through active learning. Astronomaly is a publicly available framework for active anomaly detection in astronomy. It learns to make recommendations of interesting sources to a user, allowing scientists to quickly uncover rare and new classes of objects in large datasets. In this paper, we review some of the discoveries made using Astronomaly, discuss the latest deep learning-based approaches and explore the critical role of the cyber-human interface in unveiling cosmic mysteries hidden in massive astronomical datasets.
CommentsProceedings for the IAU General Assembly 2024 (Cape Town): Focus Meeting 7, invited talk. 7 pages, 4 figure
Journal refM. Lochner, "Enabling New Discoveries with Machine Learning," Proceedings of the International Astronomical Union, vol. 20, no. A32, pp. 287-293, 2024
