Tomography from the Next Generation of Cosmic Shear Experiments for Viable f(R) Models

TL;DR

This paper investigates the cosmic shear signals predicted by two f(R) gravity models, demonstrating that the Hu & Sawicki model can be distinguished from LCDM with upcoming Euclid data, supporting f(R) theories as alternatives to dark energy.

Contribution

It provides the first detailed forecast of cosmic shear signals for Starobinsky and Hu & Sawicki f(R) models using Euclid-like data, highlighting their distinguishability from LCDM.

Findings

Hu & Sawicki model's shear signal is distinguishable from LCDM.

Starobinsky model's shear signal is nearly degenerate with LCDM.

Bayes factor analysis favors the HS model if Euclid detects n_HS > 0.02.

Abstract

We present the cosmic shear signal predicted by two viable cosmological models in the framework of modified-action f(R) theories. We use f(R) models where the current accelerated expansion of the Universe is a direct consequence of the modified gravitational Lagrangian rather than Dark Energy (DE), either in the form of vacuum energy/cosmological constant or of a dynamical scalar field (e.g. quintessence). We choose Starobinsky's (St) and Hu & Sawicki's (HS) f(R) models, which are carefully designed to pass the Solar System gravity tests. In order to further support - or rule out - f(R) theories as alternative candidates to the DE hypothesis, we exploit the power of weak gravitational lensing, specifically of cosmic shear. We calculate the tomographic shear matrix as it would be measured by the upcoming ESA Cosmic Vision Euclid satellite. We find that in the St model the cosmic shear signal is almost completely degenerate with LCDM, but it is easily distinguishable in the HS model. Moreover, we compute the corresponding Fisher matrix for both the St and HS models, thus obtaining forecasts for their cosmological parameters. Finally, we show that the Bayes factor for cosmic shear will definitely favour the HS model over LCDM if Euclid measures a value larger than ~0.02 for the extra HS parameter n_HS.