Dark matter is an elusive substance that makes up 80% of the universe. It also provides the skeleton for what cosmologists call the cosmic web, the large-scale structure of the universe that, due to its gravitational influence, dictates the motion of galaxies and other cosmic material. However, the distribution of local dark matter is currently unknown because it cannot be measured directly. Researchers must instead infer its distribution based on its gravitational influence on other objects in the universe, like galaxies.
Previous attempts to map the cosmic web started with a model of the early universe and then simulated the evolution of the model over billions of years. However, this method is computationally intensive and so far has not been able to produce results detailed enough to see the local universe. In the new study, the researchers 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.
The researchers built and trained their model using a large set of galaxy simulations, called Illustris-TNG, which includes galaxies, gasses, other visible matter, as well as dark matter. The team specifically selected simulated galaxies comparable to those in the Milky Way and ultimately identified which properties of galaxies are needed to predict the dark matter distribution. The research team then applied their model to real data from the local universe from the Cosmicflow-3 galaxy catalog. The catalog contains comprehensive data about the distribution and movement of more than 17 thousand galaxies in the vicinity of the Milky Way — within 200 megaparsecs.
The map successively reproduced known prominent structures in the local universe, including the “local sheet” — a region of space containing the Milky Way, nearby galaxies in the “local group,” and galaxies in the Virgo cluster — and the “local void” — a relatively empty region of space next to the local group. Additionally, it identified several new structures that require further investigation, including smaller filamentary structures that connect galaxies. For example, it has been suggested that the Milky Way and Andromeda galaxies may be slowly moving toward each other, but whether they may collide in many billions of years remains unclear. Studying the dark matter filaments connecting the two galaxies could provide important insights into their future.
Adapted and abridged from Source (Penn State. “Dark matter map reveals hidden bridges between galaxies.” ScienceDaily. ScienceDaily, 25 May 2021.)
Original paper: Sungwook E. Hong, Donghui Jeong, Ho Seong Hwang, Juhan Kim. Revealing the Local Cosmic Web from Galaxies by Deep Learning. The Astrophysical Journal, 2021; 913 (1): 76 DOI: 10.3847/1538-4357/abf040