Distinguishing standard and modified gravity cosmologies with machine learning

TL;DR

This paper introduces a convolutional neural network that effectively classifies standard and modified gravity cosmologies from weak-lensing maps, outperforming traditional methods especially when multiple redshifts are used.

Contribution

The study develops a novel CNN-based approach with data compression to distinguish cosmological models, including those with similar weak-lensing signatures, surpassing conventional statistics.

Findings

CNN outperforms higher-order statistics in model classification

Combining multiple redshifts improves classification accuracy

Method distinguishes models with at least 80% accuracy on noise-free data

Abstract

We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard concordance model ($\Lambda$CDM) in terms of Gaussian weak-lensing observables. An inability to distinguish viable models that are based on different physics potentially limits a deeper understanding of the fundamental nature of cosmic acceleration. For a fixed redshift of sources, we demonstrate that a machine learning network trained on simulated convergence maps can discriminate between such models better than conventional higher-order statistics. Results improve further when multiple source redshifts are combined. To accelerate training, we implement a novel data compression strategy that incorporates our prior knowledge of the morphology of typical convergence map features. Our method fully distinguishes $\Lambda$CDM from its most similar MG model on noise-free data, and it correctly identifies among the MG models with at least 80% accuracy when using the full redshift information. Adding noise lowers the correct classification rate of all models, but the neural network still significantly outperforms the peak statistics used in a previous analysis.