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. 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.
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. Next, they compare those dark matter maps to theoretical predictions in order to find which cosmological model most closely matches the data. Traditionally, this is done using human-designed statistics such as so-called correlation functions that describe how different parts of the maps are related to each other.
Such statistics, however, are limited as to how well they can find complex patterns in the matter maps. Main article: Dark matter in fiction. See Baryonic dark matter.
It is basically the same except that dark energy might depend on scale factor in some unknown way rather than necessarily being constant. Strictly speaking, electrons are leptons not baryons ; but since their number is equal to the protons while their mass is far smaller, electrons give a negligible contribution to the average density of baryonic matter. Baryonic matter excludes other known particles such as photons and neutrinos. Hypothetical primordial black holes are also generally defined as non-baryonic, since they would have formed from radiation, not matter.
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This means we can only see some of the galaxies in the Universe, which is the main reason why the night sky is dark. Just how dark can be calculated using the . Buy Dark Sky, Dark Matter (Series in Astronomy and Astrophysics) on Amazon. com ✓ FREE SHIPPING on qualified orders.
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