Using artificial intelligence to chart the universe
September 25, 2012
An image of a slice through the local universe, 370 million light years on each side. The red circles mark the positions of galaxies observed with the 2MRS survey, which measured the positions and distances of more than 45,000 galaxies. The blue circles are random points (galaxies) inserted to smooth the map across the “zone of avoidance,” where nearby gas and dust in our Galaxy blocks the view of more distant objects. These data are superimposed on the light and dark background of the cosmic web of galaxies modeled by Kitaura et al using an AI algorithm. (Credit: Francisco Kitaura, Leibniz Institute for Astrophysics, Potsdam)
Astronomers at the Leibniz Institute for Astrophysics have developed an AI algorithm to help them chart and explain the distribution of dark matter with unprecedented accuracy.
Cosmic microwave background radiation (credit: NASA)
The algorithm starts with the fluctuations in the density of the universe seen in the cosmic microwave background radiation (CMBR), then models the way matter collapses into today’s galaxies over the subsequent 13 billion years. The results of the algorithm are a close fit to the observed distribution and motion of galaxies.
According to Leibniz Institute for Astrophysics astrophysicist Francisco Kitaura, “Our precise calculations show that the direction of motion and 80% of the speed of the galaxies that make up the Local Group can be explained by the gravitational forces that arise from matter up to 370 million light years away.
“In comparison, the Andromeda Galaxy, the largest member of the Local Group, is a mere 2.5 million light years distant, so we are seeing how the distribution of matter at great distances affects galaxies much closer to home.”
“Our results are also in close agreement with the predictions of the Lambda Cold Dark Matter (LCDM) model. To explain the rest of the 20% of the speed, we need to consider the influence of matter up to about 460 million light years away, but at the moment the data are less reliable at such a large distance.
‘Despite this caveat, our model is a big step forward. With the help of AI, we can now model the universe around us with unprecedented accuracy and study how the largest structures in the cosmos came into being.”
Background
Scientists routinely use large telescopes to scan the sky, mapping the coordinates and estimating the distances of hundreds of thousands of galaxies and so enabling them to create a map of the large-scale structure of the Universe. But the distribution that astronomers see is hard to explain, with galaxies forming a complex “cosmic web” showing clusters, filaments connecting them, and large empty regions in between.
The driving force for such a rich structure is gravitation. This force originates from two components: the 5% of the universe that appears to be made of “normal” matter that makes up the stars, planets, dust and gas we can see, and the 23% made up of invisible “dark” matter.
Alongside thesem, some 72% of the cosmos is made up of a mysterious “dark energy” that rather than exerting a gravitational pull is thought to be responsible for accelerating the expansion of the universe. Together these three constituents are described in the LCDM model for the cosmos, the starting point for the work of the Potsdam team.
Measurements of the residual heat from the Big Bang — the cosmic microwave background radiation (CMBR) emitted 13.7 billion years ago — allow astronomers to determine the motion of the Local Group, the cluster of galaxies that includes our Milky Way galaxy. Astronomers try to reconcile this motion with that predicted by the distribution of matter around us and its associated gravitational force, but this is compromised by the difficulty of mapping the dark matter in the same region.