H-Alpha Anomalyzer: An Explainable Anomaly Detector for Solar H-Alpha Observations

Abstract--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 Hα 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. Millions of Hα images are produced by the NSF's Global Oscillation Network Group (GONG, [7]), some of which show anomaly patterns and can't be used by either algorithms or scientists. Detecting these images and removing them from the pipeline is a laborious task, especially on such a large scale. Motivated by this, we aim to analyze different solutions for cleaning corrupt Hα images and do a comprehensive quantitative and qualitative analysis of their performance.