KiDS+GAMA: Constraints on Horndeski gravity from combined large-scale structure probes
Abstract
We present constraints on Horndeski gravity from a combined analysis of cosmic shear, galaxy-galaxy lensing and galaxy clustering from of the Kilo-Degree Survey (KiDS) and the Galaxy And Mass Assembly (GAMA) survey. The Horndeski class of dark energy/modified gravity models includes the majority of universally coupled extensions to CDM with one scalar field in addition to the metric. We study the functions of time that fully describe the evolution of linear perturbations in Horndeski gravity. Our results are compatible throughout with a CDM model. By imposing gravitational wave constraints, we fix the tensor speed excess to zero and consider a subset of models including e.g. quintessence and theories. Assuming proportionality of the Horndeski functions and (kinetic braiding and the Planck mass run rate, respectively) to the dark energy density fraction , we find for the proportionality coefficients and . Our value of is in better agreement with the estimate when measured in the enlarged Horndeski parameter space than in a pure CDM scenario. In our joint three-probe analysis we report a downward shift of the best fit value from the measurement of in Horndeski gravity, compared to in CDM. Our constraints are robust to the modelling uncertainty of the non-linear matter power spectrum in Horndeski gravity. Our likelihood code for multi-probe analysis in both CDM and Horndeski gravity is publicly available at http://github.com/alessiospuriomancini/KiDSHorndeski .
Keywords
Cite
@article{arxiv.1901.03686,
title = {KiDS+GAMA: Constraints on Horndeski gravity from combined large-scale structure probes},
author = {Alessio Spurio Mancini and Fabian Köhlinger and Benjamin Joachimi and Valeria Pettorino and Björn Malte Schäfer and Robert Reischke and Edo van Uitert and Samuel Brieden and Maria Archidiacono and Julien Lesgourgues},
journal= {arXiv preprint arXiv:1901.03686},
year = {2019}
}
Comments
18 pages, 7 figures, matches version published in MNRAS. Likelihood code available at http://github.com/alessiospuriomancini/KiDSHorndeski