为太阳H-Alpha观测设计可解释的异常检测算法，提升数据质量
H-Alpha Anomalyzer: An Explainable Anomaly Detector for Solar H-Alpha Observations
- 基于用户定义规则的轻量级非机器学习检测方法
- 在2000张图像上实现优于现有方法的异常识别性能
- 明确标注异常区域并量化异常概率，适合天文学家审核
空间和地面天文台的爆发式增长带来了海量数据，需借助先进算法进行大规模处理。确保输入机器学习模型的数据质量至关重要。来自GONG网络的太阳H-α观测自2010年起每分钟产生多组数据，全天候运行。本文提出一种轻量级（非机器学习）异常检测算法H-Alpha Anomalyzer，根据用户设定标准识别异常观测。与多数黑箱算法不同，该方法能精确指出触发异常的区域，并量化异常可能性。我们还构建并发布了包含2000张观测图像的数据集，其中异常与正常样本各占一半。结果表明，该方法不仅性能优于现有技术，且具备可解释性，支持领域专家进行定性评估。
原文摘要 · Abstract (English)
The plethora of space-borne and ground-based observatories has provided astrophysicists with an unprecedented volume of data, which can only be processed at scale using advanced computing algorithms. Consequently, ensuring the quality of data fed into machine learning (ML) models is critical. The H$α$ observations from the GONG network represent one such data stream, producing several observations per minute, 24/7, since 2010. In this study, we introduce a lightweight (non-ML) anomaly-detection algorithm, called H-Alpha Anomalyzer, designed to identify anomalous observations based on user-defined criteria. Unlike many black-box algorithms, our approach highlights exactly which regions triggered the anomaly flag and quantifies the corresponding anomaly likelihood. For our comparative analysis, we also created and released a dataset of 2,000 observations, equally divided between anomalous and non-anomalous cases. Our results demonstrate that the proposed model not only outperforms existing methods but also provides explainability, enabling qualitative evaluation by domain experts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。