Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

2
The categories of this paper are: math
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph