Year: 2020
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Murakami, Koya, Nishizawa, Atsushi J.
Institution: Nagoya University
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G2710432281, G3851218966, G5987394252
OpenAlex ID: W3110778797