Machine Learning Helps Penetrate Galaxy’s Mysteries

Machine Learning Helps Penetrate Galaxy’s Mysteries By SEAN DUFFY       (CN) – A machine learning simulation process developed by researchers from the University of Illinois could make galaxy modeling more efficient and offer astronomers an opportunity to derive new insights into how the universe evolves.     Galaxy simulation ordinarily requires countless computing hours, making the process costly and inefficient. To develop accurate models that derive new findings, numerous simulations that factor in known and unknown variables must be run.     “When we make a cosmological measurement, we have some implicit uncertainty since we can’t rerun the experiment,” University of Illinois astronomy professor and process co-author Robert Brunner said. “One way to characterize this error is by simulating multiple universes and seeing how the measurements change. But this is a computationally expensive prospect, and takes a…


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