Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height

Solar flares, caused by magnetic field reconnection in the sun’s atmosphere, are intense bursts of electromagnetic radiation that can disrupt the Earth’s space environment, affecting communication systems, GPSs, and satellites. Traditional physics-based methods for solar flare forecasting have utilized the statistical relationships between solar activity indicators, such as sunspots and magnetic field properties, employing techniques like Poisson distributions and discriminant analysis to estimate probabilities and identify critical parameters. While these methods provide valuable insights, limitations in predictive accuracy have driven the integration of deep learning approaches. With the accumulation of solar observation data and the development of data-driven algorithms, deep learning methods have been widely used to build solar flare prediction models. Most research has focused on designing or selecting the right deep network for the task. However, the influence of the magnetic field height on deep-learning-based prediction models has not been studied. This paper investigates how different magnetic field heights affect solar flare prediction performance. Active regions were observed using HMI magnetograms from 2010 to 2019. The magnetic field heights were stratified to create a database of active regions, and deep neural networks like AlexNet, ResNet-18, and SqueezeNet were used to evaluate prediction performance. The results show that predictions at around 7200 km above the photosphere outperform other heights, aligning with physical method analysis. At this altitude, the average AUC of the predictions from the three models reaches 0.788.