A general framework for unbiased tests of gravity using galaxy clusters [CEA]

http://arxiv.org/abs/2110.14564


We present a Markov chain Monte Carlo pipeline which can be used for unbiased large-scale tests of gravity using galaxy cluster observations. The pipeline, which currently uses cluster number counts to constrain the present-day background scalar field $f_{R0}$ of Hu-Sawicki $f(R)$ gravity, fully accounts for the effects of the fifth force on cluster properties including the dynamical mass, the halo concentration and the observable-mass scaling relations. This is achieved using general models which have been calibrated over a wide and continuous mass range ($10^{11}M_{\odot}\lesssim M\lesssim10^{15}M_{\odot}$) using a large suite of cosmological simulations, including the first to simultaneously incorporate both screened modified gravity and full baryonic physics. We show, using mock cluster catalogues, that an incomplete treatment of the observable-mass scaling relations in $f(R)$ gravity, which do not necessarily follow the usual power-law behaviour, can lead to unbiased and imprecise constraints. It is therefore essential to fully account for these effects in future cosmological tests of gravity that will make use of vast cluster catalogues from ongoing and upcoming galaxy surveys. Our constraint framework can be easily extended to other gravity models; to demonstrate this, we have carried out a similar modelling of cluster properties in the normal-branch Dvali-Gabadadze-Porrati model (nDGP), which features a very different screening mechanism. Using our full-physics simulations, we also study the angular power spectra of the thermal and kinetic Sunyaev-Zel’dovich effects in $f(R)$ gravity and nDGP, and demonstrate the potential for precise constraints of gravity using data from upcoming CMB experiments. Finally, we present a retuned baryonic physics model, based on the IllustrisTNG model, which can be used for full-physics simulations within large cosmological volumes.

Read this paper on arXiv…

M. Mitchell
Thu, 28 Oct 21
43/76

Comments: PhD thesis, 289 pages, 69 figures, 7 tables