  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
near-infrared 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 non-linear
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 (FCN) that incorporates spatial
information and accounts for the projection of the spherical Sun to 2-d images.
Using normalized target values, results indicate a median pixel-wise 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.
