It’s been nearly a century since astronomer Fritz Zwicky first calculated the mass of the Coma Cluster, a dense collection of almost 1,000 galaxies located in the nearby universe. But estimating the mass of something so huge and dense, not to mention 320 million light-years away, has its share of problems—then and now. Zwicky’s initial measurements, and the many made since, are plagued by sources of error that bias the mass higher or lower.
Now, using tools from machine learning, a team led by Carnegie Mellon University physicists has developed a deeplearning method that accurately estimates the mass of the Coma Cluster and effectively mitigates the sources of error.
“People have made mass estimates of the Coma Cluster for many, many years...
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