A new map of dark matter made using AI (Artificial intelligence) reveals hidden filaments of the invisible stuff bridging galaxies.
The map focuses on the local universe — the neighborhood surrounding the Milky Way . Despite being near by, the local universe is difficult to map because it’s chock filled with complex structures made from visible matter, said Donghui Jeong, an astrophysicist at Pennsylvania State University and therefore the lead author of the new research.
“We need to reverse engineer to understand where dark matter is by watching galaxies,” Jeong said.
Dark matter may be a mysterious, invisible substance that interacts with visible matter via gravity. Some researchers theorize that this invisible matter might contains weakly interacting massive particles, or WIMPs, which might be very large (for subatomic particles, anyway) and electromagnetically neutral, in order that they would not interact with anything on the EM (electro magnetic) spectrum , like light. Another idea with some potential evidence to back it up is that substance (dark matter) might contains ultralight particles called axions.
Whatever dark matter is, its effects are detectable within the gravitational forces permeating the universe. Mapping out an invisible gravity isn’t easy, though. Typically, researchers roll in the hay by running large computer simulations, starting with a model of the early universe and fast-forwarding through billions of years of expansion and evolution of visible matter, filling within the gravitational blanks to find out where substance was and where it should be today. this needs major computing power and significant amounts of time , Jeong said.
This latest study takes a special approach. The researchers first trained a machine-learning program on thousands of computer simulations of visible matter and substance within the local universe. Machine learning may be a technique that’s particularly adept at picking out patterns from large datasets. The model universes within the study came from a classy set of simulations called Illustris-TNG.
After testing the machine-learning algorithm’s training on a second set of Illustris-TNG universe simulations for accuracy, the researchers applied it to real-world data. They used the Cosmicflows-3 galaxy catalog, which holds data on the distribution and movement of the visible matter within 200 megaparsecs, or 6.5 billion light-years, of the Milky Way . That area includes quite 17,000 galaxies.
The result was a latest map of dark matter within the local universe and its relationships to visible matter. In a promising finding, the machine-learning algorithm reproduced much of what was already known or suspected about the Milky Way’s neighborhood from cosmological simulations. But it also suggested new features, including long filaments of substance (dark matter) that connect galaxies round the Milky Way thereto and to at least one another.
This is important for understanding how galaxies will move’ over time, Jeong said. for instance , the Milky Way and therefore the Andromeda galaxies are expected to crash into one another in about 4.5 billion years. Understanding local dark matter’s role therein collision could help address more precisely how and when that merger — will occur.
“Now that we all know the distribution of dark matter we might calculate more accurately the acceleration which will move the galaxies around us,” Jeong said.
The research appeared in the Astrophysical Journal.