AI Discovers a Secret Equation for Considering Galaxy Clusters in Colossal Clusters

AI Discovers a Secret Equation for Considering Galaxy Clusters in Colossal Clusters ...

This aerial view from the Hubble Space Telescope shows a spiral galaxy (bottom left) in front of a large galaxy cluster. New research utilized an artificial tool to estimate galaxy masses more accurately.

Artificial intelligence (AI) has been employed by astronomers at the Institute for Advanced Study and the Flatiron Institute to improve the method of calculating the mass of massive galaxies. Researchers now are capable of making far more accurate mass estimates than before by incorporating a simple term into an existing equation.

According to a study by the astrophysicists, newly improved calculations will enable scientists to better understand the universe's basic features.

"It's such a simple thing," says study co-author Francisco Villaescusa-Navarro, a research scientist at the Flatiron Institute's Center for Computational Astrophysics in New York City. "Even though it's so simple, nobody before discovered this term."

Digvijay Wadekar of the Institute for Advanced Study in Princeton, New Jersey, alongside researchers from the CCA, Princeton University, Cornell University, and the Center for Astrophysics | Harvard & Smithsonian

Understanding the universe requires knowledge of where and how much stuff there is. Galaxy clusters are the most massive objects in the universe: A single cluster may contain anything from hundreds to tens of thousands of galaxies, as well as plasma, hot gas, and dark matter. These components are held together by the gravity of the cluster.

The most essential element in determining the properties of a galaxy cluster is its total mass. However, this quantity cannot be 'weighed' by putting them on a scale. The issue is even more complicated when the dark matter that makes up much of a cluster's mass is invisible. Instead, scientists deduce the mass of a cluster from other observable quantities.

The results of the new symbolic regression equation are shown in the middle panel, while the results of the old method are shown in the top. The lower panel explicitly quantifies the scatter reduction.

Rashid Sunyaev, a current distinguished visiting professor at the Institute for Advanced Study's School of Natural Sciences, and Yakov B. Zel'dovich have developed a new method to estimate galaxy cluster masses. The properties of these new photons depend on how much pressure the galaxy cluster has exerted.

Wadekar and his colleagues suggested that a'symbolic regression' technique that utilizes various mathematical operators to test whether the data is correct.

Wadekar and his collaborators 'fed' their AI program a state-of-the-art universe simulation containing many galaxy clusters. Next, their program, written by CCA research fellow Miles Cranmer, sought for and identified additional variables that might enhance the mass estimates.

AI is powerful at identifying new parameter combinations that human analysts may overlook. For example, while it is easy for humans to identify two significant parameters in a dataset, AI can interpret large volumes, often uncovering unexpected influencers.

"Dark neural networks are very powerful, but their drawback is that we cannot see what goes on in them. In physics, we want to know why something is doing well."

Wadekar and his colleagues modified the existing algorithm to obtain a more reliable mass inferences. They found that gas concentration correlates with the areas of galaxy clusters where supermassive black holes lurk.

The researchers applied the AI-discovered formula to tens of thousands of simulated universes from the CCA's CAMELS suite. They found that the large cluster size estimate decreased the variability in galaxy cluster mass estimates by about 20 to 30 percent compared to the currently used one.

The new equation can help observational astronomers participating in upcoming galaxy cluster surveys obtain better insights into the mass of the objects they observe. “Examples include the Simons Observatory, the Stage 4 CMB experiment, and an X-ray survey called eROSITA.

Wadekar believes this paper will illustrate the potential for symbolic regression in astrophysics. “With these tools, you can explore small astrophysical structures, such as exoplanets,” he said.

Digvijay Wadekar, Leander Thiele, J. Colin Hill, David N. Spergel, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars Hernquist, and Shirley Ho, Proceedings of the National Academy of Sciences, 17 March 2023

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