Cosmological model discrimination with Deep Learning

TL;DR

This paper demonstrates that Deep Learning, specifically a Deep Convolutional Neural Network, can effectively distinguish between cosmological models using weak lensing mass maps, outperforming traditional non-Gaussian statistics especially under noisy conditions.

Contribution

The authors develop a novel DCNN approach with a new training strategy that robustly discriminates cosmological models from weak lensing data, surpassing traditional methods in noisy environments.

Findings

DCNN outperforms skewness and kurtosis in model discrimination.

The method maintains over 85% efficiency at high noise levels.

It effectively breaks the $\sigma_8$ - $\Omega_m$ degeneracy with weak lensing maps.

Abstract

We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our method to be able to distinguish between five models, which were chosen to lie along the $\sigma_8$ - $\Omega_m$ degeneracy, and have nearly the same two-point statistics. We design and implement a Deep Convolutional Neural Network (DCNN) which learns the relation between five cosmological models and the mass maps they generate. We develop a new training strategy which ensures the good performance of the network for high levels of noise. We compare the performance of this approach to commonly used non-Gaussian statistics, namely the skewness and kurtosis of the convergence maps. We find that our implementation of DCNN outperforms the skewness and kurtosis statistics, especially for high noise levels. The network maintains the mean discrimination efficiency greater than $85\%$ even for noise levels corresponding to ground based lensing observations, while the other statistics perform worse in this setting, achieving efficiency less than $70\%$. This demonstrates the ability of CNN-based methods to efficiently break the $\sigma_8$ - $\Omega_m$ degeneracy with weak lensing mass maps alone. We discuss the potential of this method to be applied to the analysis of real weak lensing data and other datasets.