Solar Flare Prediction Using a Hybrid Convolutional Neural Network and Transformer Model

Abstract

Solar flares are intense bursts of electromagnetic radiation that occur when stored magnetic energy in the Sun's atmosphere is suddenly released. They are categorized into five classes -- from least to most powerful: A, B, C, M, and X -- with each successive class representing a ten-fold increase in energy output. The electromagnetic radiation emitted by the stronger classes (C, M, and X) is capable of causing significant disruptions to communication systems, satellites, and power grids on Earth. Accurate prediction of solar flares is crucial for mitigating their adverse effects and ensuring the functionality of critical infrastructure. This research introduces a novel model named ResNet-Transformer, which combines a convolutional neural network (CNN), ResNet50, with a standard Transformer architecture. The hybrid model effectively processes both spatial and time-series data derived from solar images to predict the occurrence, class (C, M, and X), and probability of solar flares within 24-hour, 36-hour, and 72-hour windows. This hybrid deep learning model represents the first of its kind in the domain of image-based, multiclass solar flare prediction. We evaluated the model's performance using a comprehensive set of metrics, including weighted precision, recall, and F1 score, together with balanced accuracy, the Matthews correlation coefficient (MCC), Cohen's kappa, and the area under the receiver operating characteristic curve (ROC-AUC). Results show that ResNet-Transformer surpasses traditional machine learning methods, such as support vector machines (SVMs) and standalone CNN models, across all evaluated metrics. This study highlights the potential of integrating convolutional neural networks with Transformers to enhance predictive capabilities in solar physics, paving the way for more reliable and timely solar flare forecasting.