An AI-assisted analysis of three-dimensional galaxy distribution in our universe

By applying a machine-learning technique, a neural network method, to gigantic amounts of simulation data about the formation of cosmic structures in the universe, a team of researchers has developed a very fast and highly efficient software program that can make theoretical predictions about structure formation. By comparing model predictions to actual observational datasets, the team succeeded in accurately measuring cosmological parameters, reports a study in Physical Review D. When the biggest galaxy survey to date in the world, the Sloan Digital Sky Survey (SDSS), created a three-dimensional map of the universe via the observed distribution of galaxies, it became clear that galaxies had certain characteristics. Some would clump together, or spread out in filaments, and in some places there were voids where no galaxies existed at all. All these show galaxies did not evolve in a uniform way, they formed as a result of their local environment. In general, researchers agree this non-uniform distribution of galaxies is because of the effects of gravity caused by the distribution of "invisible" dark matter, the mysterious matter that no one has yet directly observed. By studying the data in the three-dimensional map of galaxies in detail, researchers could uncover the fundamental quantities such as the amount of dark matter in the universe.