Proxy-based Prediction of Solar Extreme Ultraviolet Emission Using Deep Learning

Abstract

High-energy radiation from the Sun governs the behavior of Earth's upper atmosphere and such radiation from any planet-hosting star can drive the long-term evolution of a planetary atmosphere. However, much of this radiation is unobservable because of absorption by Earth's atmosphere and the interstellar medium. This motivates the identification of a proxy that can be readily observed from the ground. Here, we evaluate absorption in the nearinfrared 1083 nm triplet line of neutral orthohelium as a proxy for extreme ultraviolet (EUV) emission in the 30.4 nm line of He II and 17.1 nm line of Fe IX from the Sun. We apply deep learning to model the nonlinear relationships, training and validating the model on historical, contemporaneous images of the solar disk acquired in the triplet He I line by the ground-based SOLIS observatory and in the EUV by the NASA Solar Dynamics Observatory. The model is a fully convolutional neural network that incorporates spatial information and accounts for the projection of the spherical Sun to 2d images. Using normalized target values, results indicate a median pixelwise relative error of 20% and a mean disk-integrated flux error of 7% on a held-out test set. Qualitatively, the model learns the complex spatial correlations between He I absorption and EUV emission has a predictive ability superior to that of a pixel-by-pixel model; it can also distinguish active regions from high-absorption filaments that do not result in EUV emission.

Pixel-to-pixel Translation of Solar Extreme-ultraviolet Images for DEMs by Fully Connected Networks

“…Szenicer et al (2019) presented a CNN-based model that learns mapping from EUV narrowband images to spectral irradiance measurements using SDO data. Pineci et al (2021) presented a CNN-based model to the translation from SOLIS He I images to solar EUV images. Several studies have reported a solar image translation model using a CNN model with a generative adversarial network (GAN; Goodfellow et al 2014) loss function.…”