Training a neural network to study dark matter
https://www.sciencedaily.com/releases/2019/05/190516145206.htm
Toward this end, gravitational lensing is one of the most promising tools scientists have to extract this information by giving them the ability to probe both the geometry of the universe and the growth of cosmic structure. Gravitational lensing distorts images of distant galaxies in a way that is determined by the amount of matter in the line of sight in a certain direction, and it provides a way of looking at a two-dimensional map of dark matter, according to Deborah Bard, Group Lead for the Data Science Engagement Group in Berkeley Lab's National Energy Research Scientific Computing Center (NERSC).
"Gravitational lensing is one of the best ways we have to study dark matter, which is important because it tells us a lot about the structure of the universe," she said. "The majority of matter in the universe is dark matter, which we can't see directly, so we have to use indirect methods to study how it is distributed."