Experts at the US Department of Energy's (DOE) Argonne National Laboratory (ANL), in Illinois, announce the creation of a computer model that is capable of simulating a small-scale Universe.
It can do so by taking all the laws of physics into account, and making projections based on these results. The team behind the new asset believes it may come in handy for gaining a deeper understanding of the nature of dark energy.
What astronomers are planning to do is create the largest simulation of this type to date. One of the primary goals of their study is to figure out how dark energy arose, and how it acts on the Cosmos.
According to theory, dark energy is a mysterious force that makes up more than 73 percent of the Universe's mass-energy budget. It acts by forcing matter organization centers such as clusters, superclusters and galactic walls to fly away from each other.
It is also responsible for expanding the entire Universe at an ever-accelerated pace, dictated by Hubble's Constant. Exactly how it appeared following the Big Bang is one of the most elusive mysteries in astrophysics today, Space
ANL physicists Salman Habib and Katrin Heitmann took personal offense with that, so they created the new simulation, to be operated from the Mira supercomputer. This is the third-fastest computer in the world, they say.
Hundreds of millions of particles will be introduced into the model, emulating the behavior of the bits of matter that must have existed in the early moments of the Universe. The laws of physics will be applied to the lot of them, and then researchers will simulate the passing of time.
The particles will undoubtedly accumulate in blobs under the influence of gravity, simulating galaxies, clusters and superclusters. Researchers will need a total of two weeks to model 13.75 billion years.
Ultimately, scientists will compare the results yielded by the model and the main theory of cosmology with observations collected by the best telescopes in the world.
“We are trying to look for subtle ways in which it's wrong. That’s why you need these very high-resolution, very large-scale simulations to see if the observations don't match the predictions,” Habib says.