Clusternets: a deep learning approach to probe clustering dark energy

Monthly Notices of the Royal Astronomical Society, vol. 531, pp. 1534–1545

Abstract

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 (cs) associated with clustering DE. We compare the CNN results with those from a Random Forest (RF) algorithm trained on power spectra. Varying the DE equation of state parameter wDE 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 per cent 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 wDE for three different cases: wDE ∈ {−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 wDE ≈ −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 per cent enhancement in $c_s^2$ detection accuracy in some cases.

Authors 5

1. Amirmohammad Chegeni

   Shahid Beheshti University

   Affiliation as printed

   Department of Physics , Shahid Beheshti University, Tehran1983969411 , Iran

2. Farbod Hassani corresponding

   University of Oslo

   Affiliation as printed

   Institute of Theoretical Astrophysics , Universitetet i Oslo, NO-0315 Oslo , Norway

3. Alireza Vafaei Sadr Aachen Institute of Pathology

   University of Geneva · Pennsylvania State University · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department de Physique Theorique and Center for Astroparticle Physics , University of Geneva, CH-1211 Geneva , Switzerland

   Department of Public Health Sciences , College of Medicine, The Pennsylvania State University, Hershey, PA 17033 , USA

   Institute of Pathology , University Hospital RWTH Aachen, Pauwelsstraße 30, D-52074 Aachen , Germany

   Department de Physique Theorique and Center for Astroparticle Physics, University of Geneva, CH-1211 Geneva, Switzerland

4. Nima Khosravi

   Sharif University of Technology · Shahid Beheshti University

   Affiliation as printed

   Department of Physics , Shahid Beheshti University, Tehran1983969411 , Iran

   Department of Physics , Sharif University of Technology, Tehran 11155-9161 , Iran

5. Martin Kunz

   University of Geneva

   Affiliation as printed

   Department de Physique Theorique and Center for Astroparticle Physics , University of Geneva, CH-1211 Geneva , Switzerland