Abstract: A space hurricane is a vortex-like auroral bright-spot structure that occurs in the polar cap region during quiet geomagnetic periods and is caused by the local injection of a large amount of solar wind energy into the polar ionosphere, comparable to a magnetic storm. Achieving accurate and effective identification of space hurricane events from a large amount of auroral data is essential for studies of solar wind energy injection. In this paper, a Swin Transformer model, which is used to identify space hurricanes from Defense Meteorological Satellite Program/Special Sensor Uleraviolet Spectrographic Imager (DMSP/SSUSI) images, is constructed. This model improves computation time using splitting windows and establishes inter-window information transfer channels using the Shifted Window Multi-Head Self-Attention (SW-MSA) method, achieving automatic identification of space hurricanes. The study demonstrates that the model trained using a dataset consisting of space hurricanes in the northern and southern hemispheres identifies space hurricane events more accurately. The accuracy of the Swin Transformer-based space hurricane identification model is 95.94%.

Key words: Swin Transformer, space hurricane, identification model, deep Learning