Description
Coronal rain can be a key indicator of coronal heating taking place. To resolve
the coronal heating problem it behoves us to fully investigate this link across
the full disk of the Sun. There is no lack of observational data, but currently
this data is inadequate for a complete analysis of the phenomenon to be carried
out. The AIA 304 channel provides the best dataset for coronal rain observa-
tions. However, besides the cool component from He II emission, the passband
also includes hotter coronal emission from other ions. The contribution of this
hotter emission can become comparable to that of the cool emission in off-limb
observations, leading to ambiguity when determining the temperature of struc-
tures. Conversely, IRIS/SJI 1400 provides higher resolution images with far
less ambiguity between hot and cool emission, and therefore higher contrast
between both the rain and the surrounding corona. Unfortunately, the small
field-of-view of the satellite makes it ill-suited for large scale statistical analysis
of the phenomenon.
We present a novel approach to this problem by training a CycleGAN based
algorithm to undertake a style translation between AIA 304 images and those
belonging to IRIS 1400. This produces a model which can optimally, and with-
out the need of additional data, convert AIA 304 images into those unhampered
by the large temperature ambiguity. The structures in these images are then
compared to the original IRIS 1400 images, as well as those produced from
alternative methods, to show the reliability of this method going forwards.
