Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Context. The future Euclid space satellite mission will offer an invaluable opportunity to constrain modifications to Einstein's general relativity at cosmic scales. In this paper, we focus on modified gravity models characterised, at linear scales, by a scale-independent growth of perturbations while featuring different testable types of derivative screening mechanisms at smaller non-linear scales. Aims. We considered three specific models, namely Jordan-Brans-Dicke, a scalar-tensor theory with a flat potential, the normal branch of Dvali-Gabadadze-Porrati (nDGP) gravity, a braneworld model in which our Universe is a four-dimensional brane embedded in a five-dimensional Minkowski space-time, and k-mouflage gravity, an extension of k-essence scenarios with a universal coupling of the scalar field to matter. In preparation for real data, we provide forecasts from spectroscopic and photometric primary probes by Euclid on the cosmological parameters and the additional parameters of the models, respectively, ΩBD, ΩCyrillic small letter GHEc and ϵ2,0, which quantify the deviations from general relativity. This analysis will improve our knowledge of the cosmology of these modified gravity models. Methods. The forecast analysis employs the Fisher matrix method applied to weak lensing (WL); photometric galaxy clustering (GCph), spectroscopic galaxy clustering (GCsp) and the cross-correlation (XC) between GCph and WL. For the Euclid survey specifications, we define three scenarios that are characterised by different cuts in the maximum multipole and wave number, to assess the constraining power of non-linear scales. For each model we considered two fiducial values for the corresponding model parameter. Results. In an optimistic setting at 68.3% confidence interval, we find the following percentage relative errors with Euclid alone: for log10 ΩBD, with a fiducial value of ΩBD = 800, 27.1% using GCsp alone, 3.6% using GCph+WL+XC and 3.2% using GCph+WL+XC+GCsp; for log10 ΩCyrillic small letter GHEc, with a fiducial value of ΩCyrillic small letter GHEc = 0.25, we find 93.4, 20 and 15% respectively; and finally, for ϵ2,0 = -0.04, we find 3.4%, 0.15%, and 0.14%. From the relative errors for fiducial values closer to their ∇CDM limits, we find that most of the constraining power is lost. Our results highlight the importance of the constraining power from non-linear scales.

Abstract:

Constrained cosmological simulations play an important role in modelling the local Universe, enabling investigation of the dark matter content of local structures and their formation. We introduce an internal method for quantifying the extent to which the variance of individual halo properties is suppressed by the constraints imposed on the initial conditions. We apply it to the Constrained Simulations in BORG (CSiBORG) suite of 101 high-resolution realizations across the posterior probability distribution of initial conditions from the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm. The method is based on the overlap of the initial Lagrangian patch of a halo in one simulation with those in another, measuring the degree to which the haloes' particles are initially coincident. This addresses the extent to which the imposed large-scale structure constraints reduce the variance of individual halo properties. We find consistent reconstructions of M≳1014M⊙h-1 haloes, indicating that the constraints from the BORG algorithm are sufficient to pin down the masses, positions, and peculiar velocities of clusters to high precision, though we do not assess how well they reproduce observations of the local Universe. The effect of the constraints tapers off towards lower mass, and the halo spins and concentrations are largely unconstrained at all masses. We document the advantages of evaluating halo consistency in the initial conditions and describe how the method may be used to quantify our knowledge of the halo field given galaxy survey data analysed through the lens of probabilistic inference machines such as BORG.