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Getting the most out of cosmic maps
Research led by @UmichPhysics could help put cosmology on the inside track to reaching the full potential of telescopes and other instruments studying some of the universe’s largest looming questions.
The project showcased how a new computational method gleans more information than its predecessors from maps showing how galaxies are clustered and threaded throughout the universe.
Scientists are currently using tools like DESI, the Dark Energy Spectroscopic Instrument, to generate these maps and dig deeper into the nature of dark energy, dark matter and other cosmic mysteries.
The dark side of cosmology
Even as DESI makes headlines now, scientists know they will need more advanced tools to find the answers they seek. Some are developing the next generation of instruments like DESI. @MinhNguyenAstro and his colleagues, however, are focusing on optimizing our understanding of the data we’re getting now—and in the future.
“As we move to bigger and better telescopes, we might also be throwing away more information,” said Nguyen, who helped lead the work as a Leinweber Research Fellow in the U-M Department of Physics. “While we’re collecting more data, we can also try to get more out of the data.”
Teaming up with colleagues at the Max Planck Institute for Astrophysics, or MPA, Nguyen worked with a computational framework dubbed LEFTfield to upgrade how scientists analyze the large-scale structure of the cosmos.
“In the early universe, the structure was Gaussian—like the static you would see on old TV sets,” Nguyen said. “But because of the interplay between dark energy and dark matter, the large-scale structure of the universe today isn’t Gaussian anymore. It’s more like a spider web.”
Dark energy drives the expansion of the universe, but researchers can’t directly observe it, hence the “dark” part of its name. The universe’s matter works against that expansion with its attractive force of gravity.
This preserves the fidelity of the data in a way that’s inaccessible to the standard methods, Nguyen said.
“I love the idea of field-level inference because it is, in principle, the actual thing we want to do,” said Shaun Hotchkiss, host of the online seminar series, Cosmology Talks. The series recently featured Nguyen and his co-author Beatriz Tucci, a doctoral student at MPA.
“If we’ve measured the density field, why compress the information inside of it?” Hotchkiss said. “Of course, field-level inference is therefore more difficult to do, but this hasn’t stopped Bea and Minh, and shouldn’t stop the community.”
To benchmark the performance of LEFTfield, the team calculated a cosmological parameter called sigma-8, which essentially measures the clumpiness of the universe, Nguyen explained.
Compared with standard approaches, the team’s LEFTfield method could improve the sigma-8 determination by a factor of 3.5 to 5.2.
“That’s like going from DESI to the successor of DESI,” Nguyen said. “Typically, moving between two generations of surveys would take 10 to 20 years.”
Before making that leap forward, though, there is still work to do. A vital hurdle to clear will be integrating LEFTfield with specific instruments and making sure it understands how noise and idiosyncrasies of the tools impact data as it comes in, Nguyen said.
Still, he believes the approach will prove to be a powerful asset.
“It really opens the fast track to get insights into dark energy, dark matter and general relativity—the theory that this is all based on,” Nguyen said.
The research team also included Fabian Schmidt, a cosmologist and group leader at MPA, along with staff scientist Martin Reinecke and Andrija Kostić, who worked on the project as a Ph.D. student then postdoctoral researcher.
Nguyen recently completed his fellowship at U-M and is now a research fellow at the Kavli Institute for the Physics and Mathematics of the Universe in Tokyo.
