Machine learning improves precision of key cosmological parameters

A team from the Flatiron Institute has utilized machine learning to estimate five key cosmological parameters of the Universe with improved precision. By generating 2,000 simulated universes and analyzing data from over 100,000 real galaxies, they achieved a reduction in uncertainty for the clumpiness of the Universe. Their findings, published in Nature Astronomy on August 21, enhance understanding of dark matter, dark energy, and the Big Bang.