Astronomers are beginning to use a complex set of simulations, an advanced machine learning model for the formation of galaxy clusters, and a strange relationship between galaxies to understand the origins of dark matter and dark energy.
I suspect you’ve never heard of the Sunyaev Zel’dovich effect, and that’s perfectly fine. It’s a relatively obscure cosmological trick for making maps of galaxies, groups and clusters. The effect is named after two Russian scientists who first discovered the mechanism. The effect works because we have it being Soaked in the cosmic microwave background, residual radiation formed when the universe was only 380,000 years old. This radiation is relatively cold, about 3 degrees above absolute zero, which puts the radiation in the microwave regime.
As this ancient light passes through the universe on its way to our telescopes, it sometimes passes through a cluster or cluster of galaxies. These clusters and clusters have very hot gas floating inside them. Sometimes this gas collides with a passing photon from the cosmic microwave background and boosts it to a higher energy. When we make maps for Cosmic microwave background Then we see the clusters and clusters as little hot spots a little bit higher on the background. This technology allows us to map incredibly distant populations and groups, even those that are too far away to directly monitor through other means.
Astronomers and cosmologists like to use these surveys to understand the distribution of matter in the universe, which helps us discover the nature of dark matter and dark energy. But clusters and galaxies are incredibly complex places, and we need to understand all of the physics that make the gas inside clusters and clusters hot before we can use them to mine dark matter and dark energy. One of the most important processes is feedback, where the material falls supermassive black holesbut before being swallowed up they are ejected in the form of high-energy particles and blasts of radiation into the cluster’s environment and cluster.
Cosmologists have long used very detailed simulations of these effects to understand what is happening. But to build a truly reliable model of the universe, we need many different simulations with different kinds of parameters to explore all the possibilities. Then we need to relate all those different possibilities to what we’re observing and use that to extrapolate the properties of dark matter and dark energy.
To achieve this last step, a team of researchers used the CAMELS suite of simulations, along with a sophisticated machine learning algorithm, to call dark matter And the dark energy properties of what we actually observe in the universe are influenced by Sunyaev Zel’dovich. They are now just beginning to establish those links to real observations with the Dark Energy Survey telescope at the Atacama Cosmology Telescope. The hope is that future research along these lines will provide a crucial window into the nature of these dark mysteries of the universe.
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the quote: Using Cluster Shadows to Measure the Universe (2023, January 18) Retrieved January 18, 2023 from https://phys.org/news/2023-01-shadows-clusters-universe.html
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