The universe is expanding at an ever-increasing rate, and while no one is sure
why, researchers with the Dark Energy Survey (DES) at least had a strategy for
figuring it out: They would combine measurements of the distribution of
matter, galaxies and galaxy clusters to better understand what's going on.
Reaching that goal turned out to be pretty tricky, but now a team led by
researchers at the Department of Energy's SLAC National Accelerator
Laboratory, Stanford University and the University of Arizona have come up
with a solution. Their analysis, published April 6 in Physical Review
Letters, yields more precise estimates of the average density of matter as
well as its propensity to clump together—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.
"It is one of the best constraints from one of the best data sets to date,"
says Chun-Hao To, a lead author on the new paper and a graduate student at
SLAC and Stanford working with Kavli Institute for Particle Astrophysics and
Cosmology Director Risa Wechsler.
An early goal
When DES set out in 2013 to map an eighth of the sky, the goal was to gather
four kinds of data: the distances to certain types of supernovae, or
exploding 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 put all four data sources together to improve
their estimates, but there's a snag: The distributions of matter, galaxies,
and galaxy clusters are all closely related. If researchers don't take these
relationships into account, they will end up "double counting," placing too
much weight on some data and not enough on others, To says.
To avoid mishandling all this information, University of Arizona
astrophysicist Elisabeth Krause and colleagues have developed a new model
that could properly account for the connections in the distributions of all
three quantities: matter, galaxies, and galaxy clusters. In doing so, they
were able to produce the first-ever analysis to properly combine all these
disparate data sets in order to learn about dark matter and dark energy.
Improving estimates
Adding that model into the DES analysis has two effects, To says. First,
measurements of the distributions of matter, galaxies and galaxy clusters
tend to introduce different kinds of errors. Combining all three
measurements makes it easier to identify any such errors, making the
analysis more robust. Second, the three measurements differ in how sensitive
they are to the average density of matter and its clumpiness. As a result,
combining all three can improve the precision with which the 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 sharpened the precision of previous estimates for
matter's density and clumpiness.
Now that the team can incorporate matter, galaxies and galaxy clusters
simultaneously in their analysis, adding in supernova data will be
relatively straightforward, since that kind of data is not as closely
related with the other three, To says.
"The immediate next step," he says, "is to apply the machinery to DES Year 3
data, which has three times larger coverage of the sky." This is not as
simple as it sounds: While the basic idea is the same, the new data will
require additional efforts to improve the model to keep up with the higher
quality of the newer data, To says.
"This analysis is really exciting," Wechsler said. "I expect it to set a new
standard in the way we are able to analyze data and learn about dark energy
from large surveys, not only for DES but also looking forward to the
incredible data that we will get from the Vera Rubin Observatory's Legacy
Survey of Space and Time in a few years."
Reference:
C. To et al, Dark Energy Survey Year 1 Results: Cosmological Constraints from
Cluster Abundances, Weak Lensing, and Galaxy Correlations, Physical Review
Letters (2021). DOI:
10.1103/PhysRevLett.126.141301