Dark Energy Survey physicists open new window on dark energy

The universe is growing at an ever increasing rate, and although no one knows why, researchers at the Dark Energy Survey (DES) at least had a strategy to figure it out: they would combine measurements of the distribution of matter, of galaxies and clusters of galaxies to better understand what’s going on.

Achieving that goal has proven to be quite tricky, but now a team led by researchers from the Department of Energy’s SLAC National Accelerator Laboratory, Stanford University, and the University of Arizona have found a solution. Their analysis, published on April 6 in Physical examination letters, gives more accurate estimates of the average density of matter as well as its propensity to clump – two key parameters that help physicists probe the nature of dark matter and dark energy, the mysterious substances that make up the vast majority of the universe.

“This is one of the best constraints of one of the best datasets to date,” says Chun-Hao To, lead author of the new paper and a graduate student from SLAC and Stanford working with the director of the Kavli Institute for Particle Astrophysics and Cosmology. Risa Wechsler.

An early goal

When DES set out in 2013 to map one-eighth of the sky, the goal was to collect four types of data: distances to certain types of supernovae, or explosive stars; the distribution of matter in the universe; the distribution of galaxies; and the distribution of galaxy clusters. Each tells researchers something about how the universe has evolved over time.

Ideally, scientists would pull all four data sources together to improve their estimates, but there’s a catch: the distributions of matter, galaxies, and galaxy clusters are all closely related. If researchers ignore these relationships, they will end up “counting twice,” placing too much weight on some data and not enough on others, To says.

To avoid mismanaging all this information, University of Arizona astrophysicist Elisabeth Krause and her colleagues developed a new model that could correctly account for the connections in the distributions of the three quantities: matter, galaxies and clusters of galaxies. In doing so, they were able to produce the very first analysis to properly combine all of these disparate data sets in order to learn more about dark matter and dark energy.

Improve estimates

Adding this model to DES analysis has two effects, says To. First, measurements of the distributions of matter, galaxies, and galaxy clusters tend to introduce different types of errors. The combination of the three measurements makes it easier to identify such errors, which makes the analysis more robust. Second, the three measurements differ in their sensitivity to the average density of matter and its agglutination. As a result, the combination of the three can improve the accuracy with which DES can measure dark matter and dark energy.

In the new paper, To, Krause, and colleagues applied their new methods to the first year of DES data and refined the precision of previous estimates for the density and clumping of matter.

Now that the team can incorporate matter, galaxies, and galaxy clusters simultaneously into their analysis, adding supernova data will be relatively straightforward, as this type of data is not as closely related to the other three, says. To.

“The immediate next step,” he says, “is to apply the mechanisms to the data from the third year of DES, which covers three times the sky.” It’s not as simple as it sounds: while the basic idea is the same, the new data will require additional effort to improve the model in order to keep up with the superior quality of the new data. , explains To.

“This analysis is really exciting,” said Wechsler. “I expect it will set a new standard in how we can analyze data and learn more about dark energy from large surveys, not only for DES, but also looking forward to incredible data that we will get from the investigation into the legacy of the Vera Rubin Space and Time Observatory in a few years. ”