$Λ$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum
This provides a data-driven approach for cosmology researchers to reformulate beyond-ΛCDM models into independent degrees of freedom, though it is incremental as it builds on existing VAE methods.
The authors tackled the problem of parametrizing cosmological models without first-principle theories by using a variational autoencoder to compress CMB temperature power spectra into 5 or 8 latent parameters, achieving reconstruction accuracy within Planck errors and isolating early dark energy effects into a single parameter.
Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological models given by the disentangled 'latent' representation of a variational autoencoder (VAE) trained to compress cosmic microwave background (CMB) temperature power spectra. We consider a broad range of $Λ$CDM and beyond-$Λ$CDM cosmologies with an additional early dark energy (EDE) component. We show that these spectra can be compressed into 5 ($Λ$CDM) or 8 (EDE) independent latent parameters, as expected when using temperature power spectra alone, and which reconstruct spectra at an accuracy well within the Planck errors. These latent parameters have a physical interpretation in terms of well-known features of the CMB temperature spectrum: these include the position, height and even-odd modulation of the acoustic peaks, as well as the gravitational lensing effect. The VAE also discovers one latent parameter which entirely isolates the EDE effects from those related to $Λ$CDM parameters, thus revealing a previously unknown degree of freedom in the CMB temperature power spectrum. We further showcase how to place constraints on the latent parameters using Planck data as typically done for cosmological parameters, obtaining latent values consistent with previous $Λ$CDM and EDE cosmological constraints. Our work demonstrates the potential of a data-driven reformulation of current beyond-$Λ$CDM phenomenological models into the independent degrees of freedom to which the data observables are sensitive.