Deep learning for intensity mapping observations: Component extraction
  Line intensity mapping (LIM) is an emerging observational method to study the
large-scale structure of the Universe and its evolution. LIM does not resolve
individual sources but probes the fluctuations of integrated line emissions. A
serious limitation with LIM is that contributions of different emission lines
from sources at different redshifts are all confused at an observed wavelength.
We propose a deep learning application to solve this problem. We use
conditional generative adversarial networks to extract designated information
from LIM. We consider a simple case with two populations of emission line
galaxies; H$\rm\alpha$ emitting galaxies at $z = 1.3$ are confused with [OIII]
emitters at $z = 2.0$ in a single observed waveband at 1.5 $\rm\mu$m. Our
networks trained with 30,000 mock observation maps are able to extract the
total intensity and the spatial distribution of H$\rm\alpha$ emitting galaxies
at $z = 1.3$. The intensity peaks are successfully located with 74% precision.
The precision increases to 91% when we combine the results of 5 networks. The
mean intensity and the power spectrum are reconstructed with an accuracy of
$\sim$10%. The extracted galaxy distributions at a wider range of redshift can
be used for studies on cosmology and on galaxy formation and evolution.
