More precise understanding of dark energy achieved using AI

A research team as part of the the Dark Energy Survey collaboration used artificial intelligence to research dark energy more precisely from a map of dark and visible matter in the Universe covering the last seven billion years. The new AI technique allowed researchers to use much more information from the maps than would be possible with the previous method.

The new Dark Energy Survey year five results used machine learning to obtain a new measurement that offers insights into the density of the mysterious force driving the Universe’s expansion. The results were presented recently at the 243rd meeting of the American Astronomical Society meeting. What does this all mean? Don Lincoln explains.

In the culmination of a decade’s worth of effort, the DES collaboration of scientists analyzed an unprecedented sample of more than 1,500 supernovae classified using machine learning. They placed the strongest constraints on the expansion of the universe ever obtained with the DES supernova survey. While consistent with the current standard cosmological model, the results do not rule out a more complex theory that the density of dark energy in the universe could have varied over time.

From Physics, Jan. 31, 2023: Fermilab scientists are part of a group of researchers using cross-correlation measurements combining data from the Dark Energy Survey and the South Pole Telescope to determine cosmological parameters with greater precision. The analysis involved more than 150 researchers with results published as a set of three articles in Physical Review D.