The map, developed using machine learning by an international team of astrophysicists, could allow studies of the nature of the elusive dark matter (which makes up 80% of the universe), as well as the history and future of our local universe.
The distribution of local dark matter is currently unknown because it cannot be measured directly, but its distribution can be inferred based on its gravitational influence on other objects in the universe, such as galaxies.
“Ironically, it is easier to study the distribution of dark matter much further because it reflects a very distant past, which is much less complex,” says Donghui Jeong, associate professor of astronomy and astrophysics at Penn State and corresponding author of the study. “Over time, as the large-scale structure of the universe has grown, the complexity of the universe has increased, making it inherently more difficult to measure dark matter locally.”
In this work, the researchers took a different approach than usual, 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 suite of galaxy simulations, called Illustris-TNG, which includes galaxies, gases, other visible matter, and dark matter.
The new catalog contains comprehensive data on the distribution and motion of more than 17,000 galaxies in the vicinity of the Milky Way, within 200 megaparsecs.
“Having a local map of the cosmic web opens a new chapter of cosmological study,” Jeong clarifies. “We can study how the distribution of dark matter relates 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.”
Referencia: Astrophysical Journal (2021). DOI: 10.3847/1538-4357/abf040
