Sunquake Detection

Sunquakes, seismic waves on the Sun's surface associated with solar flares, are increasingly studied using automated detection methods to overcome the limitations of manual analysis. Current research focuses on applying machine learning techniques, such as autoencoders, contrastive learning, and object detection, to analyze acoustic emission maps of solar active regions, improving both the speed and potential accuracy of sunquake identification. This automated approach promises to significantly enhance our understanding of solar flare mechanisms and their energetic consequences, leading to improved space weather forecasting and potentially mitigating the impact of solar storms on Earth.