A representation learning approach to probe for dynamical dark energy in matter power spectra

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 wavenumbers $k\in (0.01-2.5)h/\mathrm{Mpc}$ and at four redshift values $z\in (0.1,0.48,0.78,1.5)$ 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 $1\sigma$ ($2\sigma$) 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-$\Lambda$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-$\Lambda$CDM models.

arXiv comment11 pages, 5 figures. Minor changes to match version published in PRD

References in corpus (60)

• Dynamics of dark energy
• Modified Gravity and Cosmology
• The Confrontation between General Relativity and Experiment
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• 4D Gravity on a Brane in 5D Minkowski Space
• Exploring the Expansion History of the Universe
• Accelerating Universes with Scaling Dark Matter
• A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team
• In the Realm of the Hubble tension $-$ a Review of Solutions
• Models of f(R) Cosmic Acceleration that Evade Solar-System Tests
• Chameleon Cosmology
• The Simons Observatory: Science goals and forecasts
• A Review of Feature Selection Methods Based on Mutual Information
• Beyond the Cosmological Standard Model
• Power Spectrum Analysis of Three-Dimensional Redshift Surveys
• Everything You Always Wanted To Know About The Cosmological Constant Problem (But Were Afraid To Ask)
• Test of the Equivalence Principle Using a Rotating Torsion Balance
• KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints
• Progress in Lunar Laser Ranging Tests of Relativistic Gravity
• Cosmological Tests of Modified Gravity
• Symmetron Fields: Screening Long-Range Forces Through Local Symmetry Restoration
• Beyond $\Lambda$CDM: Problems, solutions, and the road ahead
• Testing General Relativity in Cosmology
• Dark Energy vs. Modified Gravity
• Torsion-balance tests of the weak equivalence principle
• An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models
• A Parameterized Post-Friedmann Framework for Modified Gravity
• Measuring the dark side (with weak lensing)
• A discriminating probe of gravity at cosmological scales
• An introduction to the Vainshtein mechanism
• HMcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback
• Parameterization of Dark-Energy Properties: a Principal-Component Approach
• Distinguishing Modified Gravity from Dark Energy
• Improved forecasts for the baryon acoustic oscillations and cosmological distance scale
• k-Mouflage gravity
• Accurate halo-model matter power spectra with dark energy, massive neutrinos and modified gravitational forces
• Current constraints on the cosmic growth history
• COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
• Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations
• DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys
• How to optimally parametrize deviations from General Relativity in the evolution of cosmological perturbations
• Cosmological Tests of General Relativity with Future Tomographic Surveys
• Investigating dark energy experiments with principal components
• Cosmological tests of General Relativity: a principal component analysis
• Dimensionality Reduction of SDSS Spectra with Variational Autoencoders
• SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry
• On the road to percent accuracy IV: ReACT -- computing the non-linear power spectrum beyond $\Lambda$CDM
• Theoretical Priors On Modified Growth Parametrisations
• Distinguishing standard and modified gravity cosmologies with machine learning
• Modeling the nonlinear clustering in modified gravity models I: A fitting formula for matter power spectrum of f(R) gravity
• Principal Component Analysis of Modified Gravity using Weak Lensing and Peculiar Velocity Measurements
• Spherical collapse, formation hysteresis and the deeply non-linear cosmological power spectrum
• Fast and accurate predictions of the nonlinear matter power spectrum for general models of Dark Energy and Modified Gravity
• CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators
• A robust estimator of mutual information for deep learning interpretability
• Capturing the physics of MaNGA galaxies with self-supervised Machine Learning
• Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
• Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra
• Parametrizations for tests of gravity
• Explaining dark matter halo density profiles with neural networks

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• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
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