用机器学习从天文图像中自动识别彗星活动，提升发现效率。
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
- 采用机器学习方法检测太阳系天体的延展性活动特征。
- 可有效区分彗星与恒星源，解决传统方法误判问题。
- 适用于未来大规模巡天如薇拉·鲁宾天文台数据。
本章探讨了在地面和空间广域全天巡天中，利用机器学习方法识别和定位太阳系天体的彗星活动。首先分析了在恒星型源存在下识别已知及未知活跃延展天体的挑战，并回顾经典非机器学习识别技术及其局限性。随后讨论了应用机器学习技术应对延展目标识别难题的方案。最后展望未来方法发展及在薇拉·鲁宾天文台等下一代巡天项目中的应用前景。
原文摘要 · Abstract (English)
In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field all-sky surveys. We will begin the chapter by discussing the challenges of identifying known and unknown active, extended Solar System objects in the presence of stellar-type sources and the application of classical pre-ML identification techniques and their limitations. We will then transition to the discussion of implementing ML techniques to address the challenge of extended object identification. We will finish with prospective future methods and the application to future surveys such as the Vera C. Rubin Observatory.
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