A team of researchers say they’ve calculated the underlying cosmological parameters of the universe by using an AI model that could supercharge efforts to analyze how the cosmos is structured.

As detailed in a study published in the journal Nature Astronomy, the researchers estimated the parameters by having the specially trained AI analyze a survey of around 110,000 galaxies, achieving a level of precision that a traditional analysis would’ve needed four times as many galaxies for.

The sought-after parameters are “essentially the ‘settings’ of the universe that determine how it operates on the largest scales,” said study co-author Liam Parker, an astrophysicist at the Flatiron Institute, in a statement about the work.

By teasing these “settings” out, scientists hope to use them to test and revise the standard model of cosmology — and possibly even explain one of its most pressing crises.

Mapping the universe’s countless galaxies takes a lot of resources. According to co-author Shirley Ho, a fellow Flatiron astrophysicist, the most extensive surveys can cost hundreds of millions or even billions of dollars each. She said the main reason we do them is to obtain those vaunted cosmological parameters.

“You want the best analysis you can to extract as much knowledge out of these surveys as possible and push the boundaries of our understanding of the universe,” Ho said in the statement.

The parameters tell us how visible matter and its invisible but hypothetical counterparts , dark matter and dark energy, organize on a cosmic scale.

For example, one parameter describes matter’s tendency to clump together — which dark matter, with its significant gravitational influence, plays an indispensable role in facilitating.

However, these values always need some fine-tuning. So, to add to the mix, the researchers began by training an AI on 2,000 simulated universes, each created with different “settings.”

This taught the model to recognize the subtle galaxy distribution effects caused by these varied parameters. The AI model also analyzed three or more galaxies at the same time to recognize the large-scale shapes that formed between them.

But crucially, the AI also became skilled at recognizing small-scale differences in galaxy clustering, which is why, when it was finally instructed to look at a real survey of 100,000 plus galaxies, it got such a precise result.

Until now, efforts to calculate the cosmological parameters haven’t been able “to go down to small scales,” said lead author ChangHoon Hahn, a cosmologist at Princeton University, in the statement. “For a couple of years now, we’ve known that there’s additional information there; we just didn’t have a good way of extracting it.”

Since it enables better results with less data, the researchers think that their technique could help solve a cosmological mystery known as the Hubble constant tension, in which the universe appears to be expanding faster than the cosmological models predicted.

“If we measure the quantities very precisely and can firmly say that there is a tension, that could reveal new physics about dark energy and the expansion of the universe,” Hahn said.

New physics is always an exciting prospect.

And scientists are very keen to know more about the vast reaches of space.

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