Deep learning insights into cosmological structure formation

Understand

The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations.

• However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle.
• Here, we build a deep learning framework to investigate this question.
• We train a three-dimensional convolutional neural network (CNN) to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses.