  A grand challenge of the 21st century cosmology is to accurately estimate the
cosmological parameters of our Universe. A major approach to estimating the
cosmological parameters is to use the large-scale matter distribution of the
Universe. Galaxy surveys provide the means to map out cosmic large-scale
structure in three dimensions. Information about galaxy locations is typically
summarized in a "single" function of scale, such as the galaxy correlation
function or power-spectrum. We show that it is possible to estimate these
cosmological parameters directly from the distribution of matter. This paper
presents the application of deep 3D convolutional networks to volumetric
representation of dark-matter simulations as well as the results obtained using
a recently proposed distribution regression framework, showing that machine
learning techniques are comparable to, and can sometimes outperform,
maximum-likelihood point estimates using "cosmological models". This opens the
way to estimating the parameters of our Universe with higher accuracy.

Cosmology and Nongalactic Astrophysics;Machine Learning;Machine Learning