A cosmic map reveals a weblike structure of galaxies. Scientists use tools like the Dark Energy Spectroscopic Instrument (DESI) to create these maps and explore dark energy, dark matter, and other cosmic mysteries. Even as DESI makes headlines, scientists are developing next-generation instruments to find more answers.
Researchers at the University of Michigan and colleagues at the Max Planck Institute for Astrophysics (MPA) used a computational framework called LEFTfield to improve how scientists analyze the large-scale structure of the cosmos. Their new method extracts more information from galaxy maps than previous methods.
This research could help cosmology reach the full potential of telescopes and other instruments studying the Universe’s biggest questions.
Minh Nguyen, who helped lead the work as a Leinweber Research Fellow in the U-M Department of Physics, said, “In the early Universe, the structure was Gaussian-like the static you would see on old TV sets. 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 is responsible for the Universe’s expansion but is not directly observable. The Universe’s matter works against that expansion with its attractive force of gravity.
Matter comes in two types: regular matter that we can observe and interact with, and dark matter that we can’t. The majority of the universe’s mass and energy is in dark matter and dark energy. Studying cosmic maps can help us understand these mysterious dark entities and their role in the universe’s structure.
Scientists precisely measure the total amount of matter in the universe
Using LEFTfield, scientists in this study demonstrated that they can extract even more information from existing cosmic maps. The team didn’t just improve existing standard methods to get extra information. Instead, they used a completely different approach.
The key difference between LEFTfield and standard methods is how it handles data.
“With a standard analysis, you can’t use the data as is. It has to be compressed,” Nguyen explained. “This makes analysis easier but loses some information.”
Standard analysis uses models to group galaxies into pairs or triplets for efficient statistical measurements. This works well for more Gaussian features, but Nguyen and his team wanted to understand the non-Gaussian Universe better by keeping the information that standard methods compress.
The new approach, field-level inference, treats cosmic maps as 3D grids. Each cube (voxel) in the grid becomes a working data element containing uncompressed information about the galaxies’ distribution and density.
Nguyen said, “This preserves the fidelity of the data in a way inaccessible to the standard methods.”
Shaun Hotchkiss, host of the online seminar series Cosmology Talks, said, “I love the idea of field-level inference because it is, in principle, the actual thing we want to do.”
“If we’ve measured the density field, why compress the information inside? Of course, the field-level inference is more difficult to do, but this hasn’t stopped Bea and Minh and shouldn’t stop the community.”
To test LEFTfield’s performance, the team calculated a cosmological parameter called sigma-8, which measures the clumpiness of the Universe. LEFTfield improved sigma-8 determination by 3.5 to 5.2 times compared to standard methods.
Nguyen explained, “That’s like going from DESI to its successor, a leap that usually takes 10 to 20 years.”
However, there’s still work to be done before making this leap. Integrating LEFTfield with specific instruments and understanding how noise and tool idiosyncrasies impact data are key challenges ahead.
Nguyen believes that their approach will prove to be a powerful asset. It really opens the fast track to insights into dark energy, dark matter, and general relativity—the theory on which this is all based.
Journal Reference:
- Nhat-Minh Nguyen, Fabian Schmidt, Beatriz Tucci3, Martin Reinecke3, and Andrija Kostić. How Much Information Can Be Extracted from Galaxy Clustering at the Field Level? Physical Review Letters. DOI: 10.1103/PhysRevLett.133.221006
Source: Tech Explorist
