Revealing the Local Cosmic Web from Galaxies by Deep Learning
  The 80% of the matter in the Universe is in the form of dark matter that
comprises the skeleton of the large-scale structure called the Cosmic Web. As
the Cosmic Web dictates the motion of all matter in galaxies and inter-galactic
media through gravity, knowing the distribution of dark matter is essential for
studying the large-scale structure. However, the Cosmic Web's detailed
structure is unknown because it is dominated by dark matter and warm-hot
inter-galactic media, both of which are hard to trace. Here we show that we can
reconstruct the Cosmic Web from the galaxy distribution using the
convolutional-neural-network-based deep-learning algorithm. We find the mapping
between the position and velocity of galaxies and the Cosmic Web using the
results of the state-of-the-art cosmological galaxy simulations, Illustris-TNG.
We confirm the mapping by applying it to the EAGLE simulation. Finally, using
the local galaxy sample from Cosmicflows-3, we find the dark-matter map in the
local Universe. We anticipate that the local dark-matter map will illuminate
the studies of the nature of dark matter and the formation and evolution of the
Local Group. High-resolution simulations and precise distance measurements to
local galaxies will improve the accuracy of the dark-matter map.
