Clusternets: A deep learning approach to probe clustering dark energy

TL;DR

This paper demonstrates that a CNN trained on matter density snapshots significantly outperforms a Random Forest in classifying clustering dark energy and detecting its sound speed, especially at larger scales.

Contribution

The study introduces a CNN-based method for probing clustering dark energy and its sound speed, showing improved accuracy over traditional power spectrum-based approaches.

Findings

CNN outperforms RF in classifying dark energy models.

Accuracy improves at larger scales for sound speed detection.

CNN achieves up to 40% better classification accuracy.

Abstract

Machine Learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) trained on matter density snapshots to distinguish clustering Dark Energy (DE) from the cosmological constant scenario and to detect the speed of sound ($c_s$) associated with clustering DE. We compare the CNN results with those from a Random Forest (RF) algorithm trained on power spectra. Varying the dark energy equation of state parameter $w_{\rm{DE}}$ within the range of -0.7 to -0.99, while keeping $c_s^2 = 1$, we find that the CNN approach results in a significant improvement in accuracy over the RF algorithm. The improvement in classification accuracy can be as high as $40\%$ depending on the physical scales involved. We also investigate the ML algorithms' ability to detect the impact of the speed of sound by choosing $c_s^2$ from the set $\{1, 10^{-2}, 10^{-4}, 10^{-7}\}$ while maintaining a constant $w_{\rm DE}$ for three different cases: $w_{\rm DE} \in \{-0.7, -0.8, -0.9\}$. Our results suggest that distinguishing between various values of $c_s^2$ and the case where $c_s^2=1$ is challenging, particularly at small scales and when $w_{\rm{DE}}\approx -1$. However, as we consider larger scales, the accuracy of $c_s^2$ detection improves. Notably, the CNN algorithm consistently outperforms the RF algorithm, leading to an approximate $20\%$ enhancement in $c_s^2$ detection accuracy in some cases.