Advancing knowledge of dark matter
Professor Csaba Balazs from the ARC Centre of Excellence for Particle Physics at the Terascale (CoEPP) is using the NCI supercomputer to advance our knowledge of dark matter particles.
Dark matter is a previously unknown kind of fundamental matter that makes up around 25 percent of the universe but is impossible to see. It helps explain the unexpectedly high gravitational cohesion measured within galaxies, but despite knowing that it exists, we still do not know much about its microscopic properties.
Computer simulations are used to understand how dark matter interacts with ordinary matter by modelling the microscopic theories of dark matter. By bringing experimental data from satellites and underground detectors to these dark matter models, Professor Balazs and his team can learn about dark matter in new ways.
“We take the experimental results and impose them as constraints on the theoretical models. What happens is that in the theoretical models we find correlations between experiments. Underground detectors exclude some theoretical parameter regions. Same with satellites, for different regions. And so we can go back to the experimentalists and guide them towards areas that are not excluded yet.”
Because there are hundreds of models that aim to describe dark matter, and it is still unknown which is correct, researchers are not sure if there are multiple dark matter particles or just one. By gradually excluding different parameter regions of various models, such as very heavy or very light particle masses based on the combined experimental data, the groups can hone in on the physical properties of dark matter particles.
Professor Balazs suggests that many models based on the supersymmetry theory of particle physics naturally predict a single dark matter particle. “Most supersymmetric models predict a non-decaying massive particle, this particle has no electric charge, it doesn’t interact strongly, only gravitationally and weakly with the rest of the world. It’s a perfect dark matter candidate.”
Dealing with the complex theories that describe dark matter involves many parameters spanning dozens of dimensions. When it comes to running the more than one million lines of code more than one million times, the calculation is impossible for any standard computer to deal with, which is where the NCI comes in.
“Without computers like NCI we couldn’t do anything. NCI is very important, it’s fuelling the whole work that we are doing,” says Professor Balazs.
Working on dark matter, he says, is “not only a large data problem, also a complex physics problem, and you do need supercomputing to do this.”