Identifying Cosmological Information in a Deep Neural Network
  A novel method images to estimate cosmological parameters based on images is
presented. In this paper, we demonstrate the use of a convolutional neural
network (CNN) for constraining the mass of dark matter particle. For this
purpose, we perform a suite of N-body simulations with different dark matter
particle masses to train CNN and estimate dark matter mass using a
density-contrast map. The proposed method is complementary to the one based on
summary statistics, such as two-point correlation function. We compare our CNN
classification results with those obtained from the two-point correlation of
the distribution of dark matter particles, and find that the CNN offers better
performance In addition, we use images made from a random Gauss simulation to
train a CNN, which is then compared with the CNN trained by N-body simulation
and two-point correlation. The random Gauss-trained CNN has comparable
performance to two-point correlation.
