Observational Constraints on Kinetic Gravity Braiding from the Integrated Sachs-Wolfe Effect

TL;DR

This work constrains kinetic gravity braiding (KGB) using the ISW-LSS cross-correlation, showing that small $n$ yields an anti-correlation with large-scale structure and is thus incompatible with data, while large $n$ reproduces ΛCDM-like behavior. By deriving the ISW-Galaxy cross-correlation under KGB and comparing with multiple catalogs, the authors obtain a stringent lower bound $n > 4.2\times 10^3$ (95% C.L.), with a noted robustness against reasonable galaxy bias assumptions. They also explore a phenomenological parametrization of the effective gravitational coupling, $G_{ m eff}/G = 1 + g_1 a^{g_2}$, and perform Fisher-matrix forecasts demonstrating that tomographic ISW-LSS measurements can markedly improve constraints on the evolution and amplitude of gravity in modified theories. Overall, the study establishes ISW-LSS cross-correlation as a powerful discriminator of long-range gravity modifications and outlines how future surveys like HSC and LSST can further tighten constraints on deviations from general relativity.

Abstract

The cross-correlation between the integrated Sachs-Wolfe (ISW) effect and the large scale structure (LSS) is a powerful tool to constrain dark energy and alternative theories of gravity. In this paper, we obtain observational constraints on kinetic gravity braiding from the ISW-LSS cross-correlation. We find that the late-time ISW effect in the kinetic gravity braiding model anti-correlates with large scale structures in a wide range of parameters, which clearly demonstrates how one can distinguish modified gravity theories from the LCDM model using the ISW effect. In addition to the analysis based on a concrete model, we investigate a future prospect of the ISW-LSS cross-correlation by using a phenomenological parameterization of modified gravity models.

Figures (3)

• Figure 1: The cross-correlation function theoretically calculated by using Eq. (\ref{['crossc']}) and the data obtained in Giannantonio08a. Each curve shows the cross-correlation function of the $\Lambda$CDM model (solid curve), the KGB model (dashed curve) with $n=5000,~1000,~100,~10,$ and $1$, from the top to the bottom, respectively.
• Figure 2: Contour of $\Delta\chi^2$ on the $g_1-g_2$ plane. The dashed curve and solid curve are the $1\sigma$ and 2$\sigma$ confidence contour-levels, respectively.
• Figure 3: The 1-sigma and 2-sigma contours in the $g_1-g_2$ plane for the future survey. We assumed ${\cal A}=1500~{\rm deg}^2$ + 4 bins (dot-dashed curve), ${\cal A}=20000~{\rm deg}^2$ (dashed curve), ${\cal A}=20000~{\rm deg}^2$ + 4 bins (solid curve). The target parameter is $g_1=0.1$ and $g_2=3$.