Using artificial intelligence (AI), scientists from a number of institutions have made a crucial breakthrough in estimating the fundamental properties of the cosmos.
ScienceDaily tells us that the research, led by Digvijay Wadekar from the Institute for Advanced Study, resulted in a novel equation that can more correctly forecast the mass of galaxy clusters.
Determining the Mass of Galaxy Clusters with AI
The overall mass of a galaxy cluster is difficult to calculate, and astronomers frequently rely on deducing it from other visible quantities.
Rashid Sunyaev and Yakov B. Zel'dovich devised a method in the early 1970s to estimate the mass of galaxy clusters using the impact of gravity on electron-photon interactions. This procedure, however, was not precise enough to fulfill the standards of modern astrophysics.
To tackle this problem, Wadekar and his team used an AI tool called "symbolic regression" to identify additional variables that could improve the mass estimates.
Symbolic regression is a way of finding a mathematical formula that best matches a data set. In this case, researchers used it to find a formula that predicts the mass of a galaxy cluster. They found a new formula that worked better than the old one, which only needed one extra part to improve it.
Read the full paper here.
The Equation Formulated by AI
The new equation was able to reduce the significance of complex cores in calculations, improving mass inference accuracy.
The researchers employed thousands of simulated universes from the Flatiron Institute's Center for Computational Astrophysics' CAMELS suite to test the usefulness of this novel equation.
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When compared to the currently used equation, the results showed that the equation reduced the variability in galaxy cluster mass estimates by around 20 to 30 percent for large clusters.
"It's such a simple thing; that's the beauty of this," Francisco Villaescusa-Navarro, a research scientist at the Flatiron Institute's Center for Computational Astrophysics (CCA) in New York City, tells Simons Foundation.
"Even though it's so simple, nobody before found this term. People have been working on this for decades, and still they were not able to find this," the researcher adds.
A Useful Discovery
Observational astronomers participating in subsequent galaxy cluster surveys can use this new equation to better understand the mass of the objects they encounter.
This discovery could help scientists compute the fundamental features of the universe more precisely, which is critical for unraveling the mysteries of the universe.
The researchers expect this will be the first of many publications utilizing symbolic regression in astrophysics. AI methods such as symbolic regression can assist academics in more efficiently analyzing enormous datasets and identifying patterns that might otherwise be difficult to detect. Scientists can continue to push the boundaries of our understanding of the cosmos with the help of AI.
"We think that symbolic regression is highly applicable to answering many astrophysical questions," Wadekar notes.
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