Saul Perlmutter
Berkeley astrophysicist Saul Perlmutter discusses the implications of the universe’s accelerating expansion at the University of Washington. (GeekWire Photo / Alan Boyle)

Big data just might give astronomers a better grip on the answer to one of the biggest questions in physics: Exactly what’s behind the mysterious acceleration in the expansion rate of the universe, also known as dark energy?

And that means the number crunchers at the University of Washington’s DIRAC Institute have their work cut out for them.

The role of data analysis in resolving the mystery came to the fore on Monday evening during a talk given at the DIRAC Institute’s first-ever open house on the UW campus. The speaker was none other than Berkeley astrophysicist Saul Perlmutter, who won a share of the Nobel Prize in physics in 2011 for finding the first evidence of dark energy.

That first evidence came from years’ worth of painstaking observations that were made for the Supernova Cosmology Project. Perlmutter and his colleagues scanned the skies, looking for a particular type of stellar explosion known as a Type 1A supernova. Such supernovae have a characteristic brightness and spectral signature, and can thus be used as “standard candles” for judging how far away they are and how fast they’re moving away from us.

By correlating their distance (as measured by brightness) and their outward velocity (as measured by the Doppler shift in the wavelength of their starlight), the research team could see how the expansion rate of the universe has varied over the course of billions of years.

The astronomers expected to find that the rate was either constant or slowing down — but they were gobsmacked to discover that the rate is speeding up. Perlmutter’s group, and a rival group led by Johns Hopkins University’s Adam Riess and Australian National University’s Brian Schmidt, published their  results simultaneously in 1998 and ended up sharing the Nobel three years later.

It didn’t take long for theorists to come up with hypotheses to explain dark energy. Some of the hypotheses propose that it’s a fundamental force that changes over time — a quality variously known as quintessence, or k-essence, or phantom energy.

Others suggest it’s simply a basic feature of empty space that has to be taken into account as a cosmological constant. That was an idea that Albert Einstein initially considered for his general theory of relativity, but later discarded.

Or it could be that general relativity is wrong, even though the theory has passed every observational test since Einstein came up with the theory in 1915.

“This is the kind of thing that scientists love,” Perlmutter said. “Physicists live for finding the universe caught in the act of doing something completely bizarre.”

The problem is, there aren’t yet enough supernova observations to determine which hypotheses stand a chance of being true, and which can be thrown out. Fortunately, more data is on the way.

More than two dozen supernovae have been detected in extremely distant galaxy clusters, thanks to a Hubble Space Telescope observational campaign known as “See Change.” So far, the details about the See Change supernovae have been “blinded” to ensure that the researchers don’t skew their findings.

“It’s too easy to fool yourself if you let yourself see the results while you’re still doing all the designing and testing,” Perlmutter explained. “I wish that more people understood why you don’t look at things and assume you know the answer. It seems to me that’s an important message for the political world as well.”

Perlmutter said the team is getting ready to unblind the results within the next month or so.

The See Change data set and other analyses using existing data could help theorists fine-tune their hypotheses, but it won’t be enough to start ruling out hypotheses. “We need the next generation of technology to do that,” Perlmutter said.

That’s an opportunity for the DIRAC Institute, where “DIRAC” stands for Data Intensive Research in Astrophysics and Cosmology. “It’s a great time to begin an institute like this,” Perlmutter said.

The institute’s researchers are gearing up to analyze huge amounts of sky survey data from the Zwicky Transient Facility in California, and the Large Synoptic Survey Telescope in Chile. The ZTF project is just starting to get observations into the pipeline, and eventually it’s expected to add 4 terabytes of data every night. The LSST, which is due to come online in the early 2020s, will produce about 20 terabytes’ worth of imagery nightly.

“I think we should expect surprises with these new data sets,” Perlmutter said.

It’s hard for anyone, even a Nobel-winning physicist, to anticipate what kind of surprises the universe will come up with. But Perlmutter hazarded a guess.

One thing to look for is a cosmic transition that occurred about 5 billion years ago, when the universe’s expansion rate started speeding up significantly.

“We’d love to see slight differences in how that happened, that would be the hallmarks of, let’s say, a decaying field that’s part of the explanation for why it’s accelerating,” Perlmutter said.

“At this stage, when you ask the theorists, they don’t give you much to go on. The differences in the theories are quite small, and then they also throw in 50 different theories that are all very similar to each other,” he said. “But I think what we’re hoping is, if you saw something that looked like some behavior that had to be explained — it would be fodder for an ‘aha’ moment.”

If the dark-energy mystery could be resolved, Perlmutter and his fellow physicists might feel more comfortable foreseeing the long-term future of the cosmos. Will it fizzle out into eternal darkness, which is currently the leading hypothesis? Or are there as-yet-undetected factors that will slow the universe to a halt, and then perhaps bring everything crashing back together in a reverse big bang?

Perlmutter can’t yet say whether even the ZTF and LSST observations will provide a rock-solid answer.

“But I’m not that disappointed,” he said. “I think a good mystery is almost as good as — well, maybe better than — a good answer.”

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