H-Alpha Anomalyzer: An Explainable Anomaly Detector for Solar H-Alpha Observations
This addresses the need for scalable, explainable anomaly detection in astrophysics data processing, though it is incremental as it applies a novel method to a specific domain.
The study tackled the problem of detecting anomalies in solar H-alpha observations from the GONG network, introducing a lightweight, non-ML algorithm that outperforms existing methods and provides explainability by highlighting anomalous regions and quantifying likelihood.
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.