Cosmological model discrimination with Deep Learning
  We demonstrate the potential of Deep Learning methods for measurements of
cosmological parameters from density fields, focusing on the extraction of
non-Gaussian information. We consider weak lensing mass maps as our dataset. We
aim for our method to be able to distinguish between five models, which were
chosen to lie along the $\sigma_8$ - $\Omega_m$ degeneracy, and have nearly the
same two-point statistics. We design and implement a Deep Convolutional Neural
Network (DCNN) which learns the relation between five cosmological models and
the mass maps they generate. We develop a new training strategy which ensures
the good performance of the network for high levels of noise. We compare the
performance of this approach to commonly used non-Gaussian statistics, namely
the skewness and kurtosis of the convergence maps. We find that our
implementation of DCNN outperforms the skewness and kurtosis statistics,
especially for high noise levels. The network maintains the mean discrimination
efficiency greater than $85\%$ even for noise levels corresponding to ground
based lensing observations, while the other statistics perform worse in this
setting, achieving efficiency less than $70\%$. This demonstrates the ability
of CNN-based methods to efficiently break the $\sigma_8$ - $\Omega_m$
degeneracy with weak lensing mass maps alone. We discuss the potential of this
method to be applied to the analysis of real weak lensing data and other
datasets.
