Representation learning approach to probe for dynamical dark energy in matter power spectra
Year: 2024
Type: article
Abstract: We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmic large-scale structure. DE-VAE is trained on matter power spectra boosts generated at wave numbers $k\ensuremath{\in}(0.01--2.5)\text{ }\text{ }h/\mathrm{Mpc}$ and at four redshift values $z\ensuremath{\in}(0.1,0.48,0.78,1.5)$ for the most typical dynamical DE parametrization with two extra parameters describing an evolving DE equation of state. The boosts are compressed to a lower-dimensional representation, which is concatenated with standard cold dark matter (CDM) parameters and then mapped back to reconstructed boosts; both the compression and the reconstruction components are parametrized as neural networks. Remarkably, we find that a single latent parameter is sufficient to predict 95% (99%) of DE power spectra generated over a broad range of cosmological parameters within $1\ensuremath{\sigma}$ ($2\ensuremath{\sigma}$) of a Gaussian error which includes cosmic variance, shot noise, and systematic effects for a stage IV-like survey. This single parameter shows a high mutual information with the two DE parameters, and these three variables can be linked together with an explicit equation through symbolic regression. Considering a model with two latent variables only marginally improves the accuracy of the predictions, and adding a third latent variable has no significant impact on the model's performance. We discuss how the DE-VAE architecture can be extended from a proof of concept to a general framework to be employed in the search for a common lower-dimensional parametrization of a wide range of beyond-$\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ models and for different cosmological datasets. Such a framework could then both inform the development of cosmological surveys by targeting optimal probes, and provide theoretical insight into the common phenomenological aspects of beyond-$\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ models.
Authors: Davide Piras, Lucas Lombriser
Institution: University of Geneva
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4400453326