2013/05/31 by D. Sapone, Domenico Sapone, Elisabetta Majerotto +4 · 29 citations
Mathematics · Physics and Astronomy · #Astrophysics #Classical mechanics #Cosmology #Cosmology and Gravitation Theories #Dark energy #Dark matter #Galaxies: Formation, Evolution, Phenomena #Galaxy #Mathematics #Observable #Physics #Quantum mechanics #Spectral density #Speed of sound #Statistical physics #Statistics #Stellar, planetary, and galactic studies #Theoretical physics #Viscosity #Volume viscosity #Weak gravitational lensing #astro-ph.CO #gr-qc
paper · pdf · doi:10.1103/physrevd.88.043503
published in Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields 88(4) (American Physical Society) · 16 pages, 8 figures; matches published version
openalex publication_date 2013/08/05 · arxiv created 2015/05/07 · arxiv updated 2015/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent work has demonstrated that it is important to constrain the dynamics of cosmological perturbations, in addition to the evolution of the background, if we want to distinguish among different models of the dark sector. Especially the anisotropic stress of the (possibly effective) dark energy fluid has been shown to be an important discriminator between modified gravity and dark energy models. In this paper we use approximate analytical solutions of the perturbation equations in the presence of viscosity to study how the anisotropic stress affects the weak lensing and galaxy power spectrum. We then forecast how sensitive the photometric and spectroscopic Euclid surveys will be to both the speed of sound and the viscosity of our effective dark energy fluid when using weak lensing tomography and the galaxy power spectrum. We find that Euclid alone can only constrain models with a very small speed of sound and viscosity, while it will need the help of other observables in order to give interesting constraints on models with a sound speed close to one. This conclusion is also supported by the expected Bayes factor between models.