Statistical study of auroral variability under different solar wind conditions based on classification using deep learning techniques

In this investigation, we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances. These images were categorized using an array of descriptors such as 'arc', 'ab' (aurora but bright), 'cloudy', 'diffuse', 'discrete', and 'clear'. Subsequently, we utilized a state-of-the-art convolutional neural network, ConvNeXt (Convolutional Neural Network Next), deploying deep learning techniques to train the model on a dataset classified into six distinct categories. Remarkably, on the test set our methodology attained an accuracy of 99.4%, a performance metric closely mirroring human visual observation, thereby underscoring the classifier’s competence in paralleling human perceptual accuracy. Building upon this foundation, we embarked on the identification of large-scale auroral optical data, meticulously quantifying the monthly occurrence and Magnetic Local Time (MLT) variations of auroras from stations at different latitudes: RANK (high-latitude), FSMI (mid-latitude), and ATHA (low-latitude), under different solar wind conditions. This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.