An international team of researchers developed a new map of dark matter, revealing previously unknown connections between galaxies, according to a press statement from Pennsylvania State University. The map is published in a paper in the Astrophysical Journal.
Developed using a machine learning model trained on real data, the new map of the local universe could provide key insight into the history and future of the universe, allowing us an unprecedented window into the far future.
Predicting the distribution of invisible dark matter
Dark matter makes up approximately 80 percent of the universe, and yet, we know precious little about it. What we do know is that its effect on the universe forms the backbone of the cosmic web and that it dictates the motion of the galaxies.
Researchers have only ever been able to infer the distribution of dark matter based on its gravitational influence on celestial objects, including galaxies.
Surprisingly, it is easier to study the distribution of dark matter much further away from our galaxy, due to the fact that the further you look out into space, the further back in time you are seeing.
"It's easier to study the distribution of dark matter much further away because it reflects the very distant past, which is much less complex," said Donghui Jeong, associate professor of astronomy and astrophysics at Penn State and a corresponding author of the study.
"Over time, as the large-scale structure of the universe has grown, the complexity of the universe has increased, so it is inherently harder to make measurements about dark matter locally," Jeong continued.
For their study, the team of researchers used machine learning to build a model using information about the known distribution and motion of galaxies — utilizing real data from the Cosmicflow-3 galaxy catalog. By mapping these visible objects, it was able to predict the distribution of dark matter.
Using this method, the team was able to map the distribution of dark matter in the local universe using a relatively computationally less intensive manner than previous attempts — which tried to map out the universe from its early days.
Opening a 'new chapter of cosmological study'
The new map revealed several new structures, including smaller filamentary structures that connect galaxies. Such findings, the researchers explained, could help us to predict events billions of years ahead of time.
For example, by studying the dark matter filaments connecting the Milky Way and its nearest galaxy, Andromeda, we could predict whether the two galaxies will eventually collide in many billions of years.
"Having a local map of the cosmic web opens up a new chapter of cosmological study," said Jeong. "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 filamentary structures directly, these hidden bridges between galaxies."
As dark matter essentially dictates the movements of the galaxies, it also dictates our fates, the researchers behind the new machine learning map explained.
Therefore, having an accurate model of dark matter essentially allows us to peer into the future of the universe and take a look at the direction in which we are heading.
Future space surveys, such as the one planned with NASA's James Webb Space Telescope, will provide even more information about dark matter, and more data points for the team's dark matter map, making it even more accurate at depicting the past and the future.