Speaker
Description
In the evolving landscape of 21st-century space science, forecasting space weather events such as solar flares and Coronal Mass Ejections (CMEs) are crucial yet challenging. Solar flares are intense bursts of radiation caused by the release of magnetic energy in active regions and are often accompanied by CMEs. These events can significantly impact Earth’s space environment, causing disruptions in radio communication, satellite operations, and power grids. Monitoring the temporal evolution of active regions and providing early warnings of solar flares is essential to mitigate these risks. Deep learning techniques have demonstrated significant success in detecting and predicting time-dependent events. By leveraging spatial data through convolution operations with temporal correlations, we introduce 3D Temporal Convolutional Networks(3DTCNs) to efficiently analyze active region patches over time, leveraging spatial and temporal correlations. Additionally, we introduce separate predictor modules based on flare classification to enhance the performance of our EoFTC-Nets (Eye-on-Flare Temporal Convolutional Networks) nowcasting system. Our results demonstrate that the proposed architecture matches or outperforms state-of-the-art approaches in the literature, achieving an accuracy exceeding 96% for a 24-hour forecasting window. Furthermore, the model is computationally efficient, consuming approximately 1.2 watts on Intel Movidius Myriad X, making it well-suited for onboard deployment and real-time space weather monitoring.