We can put a good figure on how much we know about the universe: 5%. That's how much of what's floating about in the cosmos is ordinary matter--planets and stars and galaxies and the dust and gas between them. The other 95% is dark matter and dark energy, two mysterious entities aptly named for our inability to shed light on their true nature. Previous work has begun pulling apart these dueling forces, but dark matter and dark energy remain shrouded in a blanket of questions--critically, what exactly are they? Enter the Vera C. Rubin Observatory, one of our 10 breakthrough technologies for 2025.

The largest ever map showing where dark matter can be found throughout the universe has been created by astronomers using the light from 100 million galaxies. Using artificial intelligence to analyse images of the shape and light from galaxies, astronomers from University College London and the École Normale Supérieure in Paris created a map of the invisible matter throughout the universe. Dark matter makes up about 80 per cent of all matter in the universe, but isn't directly visible, spotted instead through its interaction with other objects. Working as part of the international Dark Energy Survey (DES), they looked for light travelling to Earth from distant galaxies being distorted by the dark matter. The team says an accurate map showing the spread of dark matter can one day help answer questions including what the universe is made of and how it has evolved. Co-lead author Dr Niall Jeffrey from École Normale Supérieure, Paris, and UCL told MailOnline that they've mapped about a quarter of the southern hemisphere sky so far, finding dark matter covering seven billion light years.

Advancements in telescopes have enabled researchers to study the universe with greater detail, and to establish a standard cosmological model that explains various observational facts simultaneously. But there are many things researchers still do not understand. Remarkably, the majority of the universe is made up of dark matter and dark energy of an unknown nature. A promising avenue to solving these mysteries is studying the structure of the universe. The universe is made up of filaments where galaxies cluster together.

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.

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.