  A novel application of machine-learning (ML) based image processing
algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi
Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from
a short-exposure ASM generated from one-week observation by applying three ML
based image processing algorithms: dictionary learning, U-net, and Noise2Noise.
Although the inference based on ML is less clear compared to standard
likelihood analysis, the quality of the ASM was generally improved. In
particular, the complicated diffuse emission associated with the galactic plane
was successfully reproduced only from one-week observation data to mimic a
ground truth (GT) generated from a one-year observation. Such ML algorithms can
be implemented relatively easily to provide sharper images without various
assumptions of emission models. In contrast, large deviations between simulated
ML maps and GT map were found, which are attributed to the significant temporal
variability of blazar-type active galactic nuclei (AGNs) over a year. Thus, the
proposed ML methods are viable not only to improve the image quality of an ASM,
but also to detect variable sources, such as AGNs, algorithmically, i.e.,
without human bias. Moreover, we argue that this approach is widely applicable
to ASMs obtained by various other missions; thus, it has the potential to
examine giant structures and transient events, both of which are rarely found
in pointing observations.
