Solar Flare

Solar flares are sudden, intense bursts of energy from the Sun's surface, posing significant risks to technological infrastructure and astronauts. Current research heavily focuses on improving the accuracy and timeliness of flare prediction using machine learning, employing various deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and support vector machines (SVMs), often enhanced by techniques like contrastive learning and data augmentation to address class imbalance in datasets. These advancements aim to enhance space weather forecasting, enabling better mitigation strategies for potential disruptions to satellite communications, power grids, and other critical systems. Furthermore, research is exploring the use of synthetic data generation to address data scarcity issues and improve model training.