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A new map of local dark matter shows a “bridge” between galaxies. A team of astrophysicists from the United States and Korea has created a new dark matter distribution map using a deep learning method based on neural networks and data on the positions and speeds of galaxies in the local universe.

Local dark matter 3D density map:

The x sign in the center represents the galaxy; The dots denote galaxies and the arrows denote the approximate directions of motion obtained from the reconstructed gravitational potential gradient.

Local dark matter 3D density map: the X mark in the center shows the Milky Way; The dots denote galaxies and the arrows denote the approximate directions of motion obtained from the reconstructed gravitational potential gradient. Image Credit: Journal Science

“80% of the matter in the universe is in the form of dark matter which comprises the skeleton of a large-scale structure called a cosmic lattice,” said Dr. Donghui Jeong said. Institute of Gravitation and Cosmos at Pennsylvania State University.

“Since the cosmic lattice dictates the movement of all matter in galaxies and intergalactic media through gravity, knowing the distribution of dark matter is essential for studying the large-scale structure.”

“However, the detailed structure of the cosmic web is unknown because it is dominated by dark matter and hot intergalactic media, both difficult to detect.”

In the study, Dr. Jeong and his colleagues took a completely different approach, using machine learning to build a model that uses information about the distribution and motion of galaxies to predict the distribution of dark matter.

They built and trained their model using a large set of galaxy simulations, called Illustris-TNGs, which include galaxies, gas, other visible matter, and dark matter.

They specifically selected simulated galaxies comparable to the Milky Way and finally identified which properties of the galaxies were necessary to predict the distribution of dark matter.

“When given some information, the model can inevitably fill in the gaps that it has seen before,” said Drs. Jeong said. “Our model map does not fully fit the simulation data, but we can still reconstruct very detailed structures.”

“We found that the motion of the galaxies, including their peculiar radial speeds, as well as their distribution, significantly increased the quality of the map and allowed us to see these details.”

The researchers then applied their model to actual local universe data from the CosmicFlow-3 catalog of galaxies.

The map progressively reproduced known important structures in the local universe, including local sheets (a region of space containing the Milky Way, nearby galaxies in the local cluster, and galaxies in the Virgo cluster) and local zeros (a relatively empty field) . Location next to the local group).

In addition, he identified several new structures that require further investigation, including small filamentous structures that connect galaxies. “Having a local map of the cosmic web opens a new chapter in cosmological studies,” said Dr. Jeong.

We can study how the distribution of dark matter is related to other emission data, which will help us understand the nature of dark matter. And we can study these filamentous structures directly, these hidden bridges between galaxies. The results appear in the journal Science.

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