Cosmological parameter estimation from large-scale structure deep learning

Understand

We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy.

• The training set is based on 465 realizations of a cubic box with a side length of $256\ h^{-1}\ \rm Mpc$, sampled with $128^3$ particles interpolated over a cubic grid of $128^3$ voxels.
• These volumes have cosmological parameters varying within the flat $\Lambda$CDM parameter space of $0.16 \leq \Omega_m \leq 0.46$ and $2.0 \leq 10^9 A_s \leq 2.3$.
• The neural network takes as an input cubes with $32^3$ voxels and has three convolution layers, three dense layers, together with some batch normalization and pooling layers.