比较主动异常检测框架：从光变曲线到图像
Comparing Active Anomaly Detection Frameworks: From Light Curves to Images
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中文总结 AI 辅助
本研究系统比较了三种主动异常检测框架在光变曲线和图像数据上的性能，发现其有效性取决于数据特性，并展示了结合互补方法可高效发现异常天体。
中文摘要 AI 辅助
现代天文巡天数据量的增加和复杂性的提升，使得全面的视觉检查变得越来越不切实际，这推动了自动识别罕见和异常天体方法的发展。主动异常检测（A-AD）将无监督异常检测与迭代式用户反馈相结合，使搜索能够适应科学兴趣，同时保持对意外天体的敏感性。我们系统比较了三种A-AD框架，即Astronomaly、Protege和PineForest，在Zwicky瞬态设施光变曲线和Euclid星系图像上的表现。在基线实验中，Protege、PineForest和Astronomaly在前100个被检查的光变曲线天体中分别恢复了83、78和38颗变星，而PineForest在前100张Euclid图像中恢复了70个合并或形态扰动的星系，相比之下Astronomaly和Protege分别恢复了29个和19个。使用先前标签的实验表明，它们的有效性取决于先前样本的组成，并在不同A-AD方法之间有所差异。最后，我们将Protege和PineForest的互补行为结合在10,000张Euclid图像的混合搜索中，恢复了799个形态异常的天体。其中包括29个保留的强透镜系统和候选体，其中9个候选体在已发表的Euclid Q1强透镜目录中搜索不到对应物，以及202个视觉分类的环星系。这些结果强调了根据科学目标和数据集属性选择A-AD策略的重要性，并展示了它们在即将到来的天文巡天中高效、发现驱动的探索潜力。
英文摘要
The increasing volume and complexity of data from modern astronomical surveys are making comprehensive visual inspection increasingly impractical, motivating automated methods for identifying rare and unusual objects. Active anomaly detection (A-AD) combines unsupervised anomaly detection with iterative user feedback, allowing searches to adapt to scientific interests while retaining sensitivity to unexpected objects. We systematically compare three A-AD frameworks, Astronomaly, Protege, and PineForest, across Zwicky Transient Facility light curves and Euclid galaxy images. In baseline experiments, Protege, PineForest, and Astronomaly recovered 83, 78, and 38 variable stars, respectively, within the first 100 light-curve objects inspected, while PineForest recovered 70 merging or morphologically disturbed galaxies within the first 100 Euclid images, compared with 29 and 19 for Astronomaly and Protege. Experiments using prior labels show that their effectiveness depends on the composition of the prior sample and varies between A-AD methods. Finally, we combine the complementary behaviour of Protege and PineForest in a hybrid search of 10,000 Euclid images, recovering 799 morphologically unusual objects. These include 29 retained strong lens systems and candidates, of which 9 candidates have no counterparts in the published Euclid Q1 strong lens catalogues searched, and 202 visually classified ring galaxies. These results highlight the importance of selecting A-AD strategies according to both the scientific objective and dataset properties, and demonstrate their potential for efficient, discovery-driven exploration of forthcoming astronomical surveys.
发表机构
- University of Surrey（萨里大学）
机构由 AI 辅助整理，请以论文原文为准。
