http://arxiv.org/abs/2012.03778
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.
K. Murakami and A. Nishizawa
Tue, 8 Dec 20
64/73
Comments: 9 pages, 4 figures
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