Understanding how our universe came to be what it is today and what its final destiny will be is one of the biggest challenges in science. The awe-inspiring display of countless stars on a clear night gives us some idea of the magnitude of the problem, and yet that is only part of the story. The deeper riddle lies in what we cannot see, at least not directly: dark matter and dark energy. With dark matter pulling the universe together and dark energy causing it to expand faster, cosmologists need to know exactly how much of those two is out there in order to refine their models. Now, researchers are working to improve on standard methods for estimating the dark matter content of the universe through artificial intelligence.

If there is one thing the general public is familiar with when the use of artificial intelligence than it is facial recognition. Whether it is opening their mobile phone or the algorithms Facebook uses to find eyes or other parts of a face in images, facial recognition has become a standard. But now scientists dealing with complex questions like the composition of the universe are starting to use a modified version of the'standard' facial recognition in an attempt to discover how much of the dark matter there is in the universe and where it is possibly located. As Digital Trends and Futurity note in their reports on the subject, "physicists believe that understanding this mysterious substance is necessary to explain fundamental questions about the underlying structure of the universe." It is the researchers gathered in Alexandre Refregier's group at the Institute of Particle Physics and Astrophysics at ETH Zurich, Switzerland that has started to use deep neural network methods that lie behind facial recognition to develop new, special tools to attempt to discover what is still a secret of the universe for us. As Janis Fluri, one of the researchers working on the project told Digital Trends, "The algorithm we [use] is very close to what is commonly used in facial recognition," adding that"the beauty of A.I. is that it can learn from basically any data.

Understanding the how our universe came to be what it is today and what will be its final destiny is one of the biggest challenges in science. The awe-inspiring display of countless stars on a clear night gives us some idea of the magnitude of the problem, and yet that is only part of the story. The deeper riddle lies in what we cannot see, at least not directly: dark matter and dark energy. With dark matter pulling the universe together and dark energy causing it to expand faster, cosmologists need to know exactly how much of those two is out there in order to refine their models. At ETH Zurich, scientists from the Department of Physics and the Department of Computer Science have now joined forces to improve on standard methods for estimating the dark matter content of the universe through artificial intelligence.

At ETH Zurich, scientists from the Department of Physics and the Department of Computer Science have now joined forces to improve on standard methods for estimating the dark matter content of the universe through artificial intelligence. They used cutting-edge machine learning algorithms for cosmological data analysis that have a lot in common with those used for facial recognition by Facebook and other social media. Their results have recently been published in the scientific journal Physical Review D. While there are no faces to be recognized in pictures taken of the night sky, cosmologists still look for something rather similar, as Tomasz Kacprzak, a researcher in the group of Alexandre Refregier at the Institute of Particle Physics and Astrophysics, explains: "Facebook uses its algorithms to find eyes, mouths or ears in images; we use ours to look for the tell-tale signs of dark matter and dark energy." As dark matter cannot be seen directly in telescope images, physicists rely on the fact that all matter -- including the dark variety -- slightly bends the path of light rays arriving at the Earth from distant galaxies. This effect, known as "weak gravitational lensing," distorts the images of those galaxies very subtly, much like far-away objects appear blurred on a hot day as light passes through layers of air at different temperatures. Cosmologists can use that distortion to work backwards and create mass maps of the sky showing where dark matter is located.

This is a typical computer-generated dark matter map used by the researchers to train the neural network. A team of physicists and computer scientists at ETH Zurich has developed a new approach to the problem of dark matter and dark energy in the universe. Using machine learning tools, they programmed computers to teach themselves how to extract the relevant information from maps of the universe. Understanding how our universe came to be what it is today and what will be its final destiny is one of the biggest challenges in science. The awe-inspiring display of countless stars on a clear night gives us some idea of the magnitude of the problem, and yet that is only part of the story. The deeper riddle lies in what we cannot see, at least not directly: dark matter and dark energy.