Scientists have long been attempting to measure the mass of galaxy clusters through what is known as “integrated electron pressure.” However, this method is not always accurate, and researchers have turned to artificial intelligence (AI) to improve their measurements. A team made up of researchers from the Institute for Advanced Study, the Flatiron Institute’s Center for Computational Astrophysics, and Princeton University used an AI tool called symbolic regression to develop a new equation to measure the mass of galaxy clusters. This tool tries out different combinations of mathematical operators to see what equation best matches the data.
Upon inputting galaxy clusters into the simulation tool, the team employed AI to identify variables that could enhance the precision of mass calculations. Through this process, the AI developed a novel equation with an added term, which placed emphasis on integrated electron pressure. The researchers found that gas concentration aligned with regions of a galaxy cluster where mass estimations were less dependable, particularly in the cores of galaxies where supermassive black holes are situated.
the CCA’s statement noted that “in a sense, the galaxy cluster is like a spherical doughnut. The new equation extracts the jelly at the center of the doughnut that can introduce larger errors, and instead concentrates on the doughy outskirts for more reliable mass inferences.”
Afterward, the digital suite containing numerous simulated universes incorporated the fresh equation, leading to less variability in galaxy cluster mass estimates by 20 to 30 percent. The equation’s focus lies on the galaxy cluster’s outskirts, providing more dependable mass inferences. Francisco Villaescusa-Navarro, the CCA researcher and co-author of the study, stated, “This is such a simple concept, but that’s precisely what makes it beautiful.”