The standard model of the universe relies on just six numbers. Using a new approach powered by artificial intelligence, researchers at the Flatiron Institute and their colleagues extracted information hidden in the distribution of galaxies to estimate the values of five of these so-called cosmological parameters with incredible precision.

The results showed a significant improvement over the values produced by previous methods. Compared to conventional techniques using the same galaxy data, the approach yielded less than half the uncertainty for the parameter describing the clumpiness of the universe's matter. The AI-powered method also closely agreed with estimates of the cosmological parameters based on observations of other phenomena, such as the universe's oldest light.

The researchers present their method, the Simulation-Based Inference of Galaxies (or SimBIG), in a series of recent papers, including a new study published August 21 in Nature Astronomy.

Generating tighter constraints on the parameters while using the same data will be crucial to studying everything from the composition of dark matter to the nature of the dark energy driving the universe apart, says study co-author Shirley Ho, a group leader at the Flatiron Institute's Center for Computational Astrophysics (CCA) in New York City. That's especially true as new surveys of the cosmos come online over the next few years, she says.

Ho says each survey costs hundreds of millions to billions of dollars. The main reason these surveys exist is to better understand these cosmological parameters. 

Cosmological parameters

The six cosmological parameters describe the amount of ordinary matter, dark matter and dark energy in the universe and the conditions following the Big Bang, such as the opacity of the newborn universe as it cooled and whether a mass in the cosmos is spread out or in big clumps. The parameters "are essentially the 'settings' of the universe that determine how it operates on the largest scales," says Liam Parker, co-author of the Nature Astronomy study and a research analyst at the CCA.

One of the most important ways cosmologists calculate the parameters is by studying the clustering of the universe's galaxies. Previously, these analyses only looked at the large-scale distribution of galaxies.

ChangHoon Hahn, an associate research scholar at Princeton University and lead author of the Nature Astronomy study and his colleagues would train an AI model to determine the values of the cosmological parameters based on the appearance of simulated universes. Then, they'd show their model actual galaxy distribution observations.

Galaxy surveys

Hahn, Ho, Parker and their colleagues trained their model by showing it 2,000 box-shaped universes from the CCA-developed Quijote simulation suite, with each universe created using different values for the cosmological parameters. The researchers even made the 2,000 universes appear like data generated by galaxy surveys -- including flaws from the atmosphere and the telescopes themselves -- to give the model realistic practice.

By ingesting the simulations, the model learned over time how the values of the cosmological parameters correlate with small-scale differences in the clustering of galaxies, such as the distance between individual pairs of galaxies. 

With the model trained, the researchers presented it with 109,636 real galaxies measured by the Baryon Oscillation Spectroscopic Survey. As they hoped, the model leveraged small-scale and large-scale details in the data to boost the precision of its cosmological parameter estimates. Those estimates were so precise that they were equivalent to a traditional analysis using around four times as many galaxies.

ScienceDaily