Abstract:

The auroral intensity of the auroral oval directly reflects the distribution characteristics of auroral particle precipitation. It serves as a critical physical parameter for investigating magnetospheric dynamics and the space environment, playing a vital role in understanding the coupling mechanisms between the magnetosphere, ionosphere, and thermosphere. Recent studies have demonstrated that specific space physical parameters are closely correlated with auroral intensity; however, their explicit mathematical relationships remain poorly defined. Neural networks offer a robust alternative by modeling these complex non-linear associations through learned mapping relationships. In this study, an auroral oval intensity dataset spanning from December 1996 to January 1997 was constructed using ultraviolet auroral imagery from the Polar satellite, integrated with six key space physical parameters retrieved from the NASA OMNI database. A prediction model mapping physical parameters to auroral oval intensity was developed based on the Feature-Aware Generative Adversarial Network (FA-GAN), a text-to-image synthesis framework. The performance of the proposed model was evaluated using four metrics: Kullback-Leibler (KL) divergence, Structural Similarity (SSIM), Structural Inception Score (S_IS), and Denoising Feature Inception Distance (D_FID). The experimental results indicate that the FA-GAN-based prediction model significantly outperforms existing auroral intensity models in terms of SSIM, S_IS, and D_FID.

Key words:

ultraviolet aurora, auroral oval intensity modeling, generative adversarial network, space physics parameters