CosNAS: Enhancing estimation on cosmological parameters via neural architecture search

Abstract

A great challenge of cosmology is estimating the cosmological parameters of the universe. With the development of deep learning, scientists adopt 3D deep neural networks to estimate cosmological parameters from the large-scale dark matter distribution of the universe, but these methods are time-consuming to design and train neural networks. While neural architecture search is an emerging approach to estimate cosmological parameters with its capability of automatically designing neural networks, the 3D operations on a 3D dataset prohibit the usage of traditional neural architecture search methods, due to its overwhelming time and memory consumption. To tackle these issues, we propose an efficient method, CosNAS, that can automatically design neural networks with 2D operations to estimate the cosmological parameters. In addition, processing 3D data with 2D operations will inevitably cause the loss of spatial information, thus we propose an efficient SABlock to retain more 3D spatial information. We also customize a space-focused search space to focus on important information in the dark matter distribution. The experimental results indicate that our estimation of the cosmological parameters Ω, σ and n, can be applied to large-scale 3D dark matter distribution and speedup the network search by 800x. The average relative errors of cosmological parameter estimations are (0.00163, 0.00065, 0.00080), significantly decreasing the average error of estimation by 85.5% compared to previous work.