Gigantic galaxy clusters can finally be 'weighed', thanks to new AI tool

Therefore, what humans can’t comprehend, artificial intelligence can. 

Astrophysicists at the Institute for Advanced Study and the Flatiron Institute used AI to develop a better equation for calculating the precise mass of these colossal cosmic structures. 

Interestingly, the AI model known as “symbolic regression" simply tweaked the existing equation, and viola — the measurements became much more accurate.

The model was simulated using galaxy clusters and an AI-discovered equation. When compared to the existing equation, the new equation reduced the variability in huge galaxy cluster mass by around 20 to 30 percent, said the statement.

What’s more, the AI tool was also able to identify "additional variables" that could potentially improve the accuracy of the mass measurement.

“We think that symbolic regression is highly applicable to answering many astrophysical questions. In a lot of cases in astronomy, people make a linear fit between two parameters and ignore everything else. But nowadays, with these tools, you can go further. Symbolic regression and other artificial intelligence tools can help us go beyond existing two-parameter power laws in a variety of different ways, ranging from investigating small astrophysical systems like exoplanets, to galaxy clusters, the biggest things in the universe,” said Digvijay Wadekar of the Institute for Advanced Study in Princeton, who led this work. 

Understanding the mass of galaxy clusters is critical to advance our knowledge about the origin and evolution of the universe. And this development is unquestionably a step in the right direction.

The results have been outlined in the journal Proceedings of the National Academy of Sciences.

Study abstract:

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux−cluster mass relation (YSZ − M), the scatter in which affects inference of cosmological parameters from cluster abundance data.