Construction of a far ultraviolet all sky map from an incomplete survey:
  Application of a deep learning algorithm
  We constructed a far ultraviolet (FUV) all sky map based on observations from
the Far Ultraviolet Imaging Spectrograph (FIMS) aboard the Korean
microsatellite STSAT-1. For the ~20% of the sky not covered by FIMS
observations, predictions from a deep artificial neural network were used.
Seven datasets were chosen for input parameters, including five all sky maps of
H-alpha, E(B-V), N(HI), and two X-ray bands, with Galactic longitudes and
latitudes. 70% of the pixels of the observed FIMS dataset were randomly
selected for training as target parameters and the remaining 30% were used for
validation. A simple four-layer neural network architecture, which consisted of
three convolution layers and a dense layer at the end, was adopted, with an
individual activation function for each convolution layer; each convolution
layer was followed by a dropout layer. The predicted FUV intensities exhibited
good agreement with Galaxy Evolution Explorer (GALEX) observations made in a
similar FUV wavelength band for high Galactic latitudes. As a sample
application of the constructed map, a dust scattering simulation was conducted
with model optical parameters and a Galactic dust model for a region that
included observed and predicted pixels. Overall, FUV intensities in the
observed and predicted regions were reproduced well.
