Distinguishing Coupled Dark Energy Models with Neural Networks

TL;DR

This paper demonstrates that neural networks can effectively distinguish between standard and coupled dark energy models using simulated large-scale structure growth data, aiding in probing deviations from general relativity.

Contribution

The study introduces a neural network classifier trained on simulated growth-rate data to differentiate between b4CDM and coupled dark energy models, achieving high accuracy in model identification.

Findings

NN confidently detects non-zero coupling with >86% accuracy

NN distinguishes b4CDM and coupled models with up to 99% accuracy

Pipeline enhances analysis of growth-rate data for testing gravity theories

Abstract

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $\Lambda$ cold dark matter (CDM) model and a tomographic coupled dark energy (CDE) model. We built an NN classifier and tested its accuracy in distinguishing between cosmological models. For our dataset, we generated $f\sigma_8(z)$ growth-rate observables that simulate a realistic Stage IV galaxy survey-like setup for both $\Lambda$CDM and a tomographic CDE model for various values of the model parameters. We then optimised and trained our NN with \texttt{Optuna}, aiming to avoid overfitting and to maximise the accuracy of the trained model. We conducted our analysis for both a binary classification, comparing between $\Lambda$CDM and a CDE model where only one tomographic coupling bin is activated, and a multi-class classification scenario where all the models are combined. For the case of binary classification, we find that our NN can confidently (with $>86\%$ accuracy) detect non-zero values of the tomographic coupling regardless of the redshift range at which coupling is activated and, at a $100\%$ confidence level, detect the $\Lambda$CDM model. For the multi-class classification task, we find that the NN performs adequately well at distinguishing $\Lambda$CDM, a CDE model with low-redshift coupling, and a model with high-redshift coupling, with 99\%, 79\%, and 84\% accuracy, respectively. By leveraging the power of machine learning, our pipeline can be a useful tool for analysing growth-rate data and maximising the potential of current surveys to probe for deviations from general relativity.