Dark Energy Survey year 1 results: Constraints on extended cosmological models from galaxy clustering and weak lensing

Abstract

We present constraints on extensions of the minimal cosmological models dominated by dark matter and dark energy, ΛCDM and wCDM, by using a combined analysis of galaxy clustering and weak gravitational lensing from the first-year data of the Dark Energy Survey (DES Y1) in combination with external data. We consider four extensions of the minimal dark energy-dominated scenarios: (1) nonzero curvature Ω k , (2) number of relativistic species N eff different from the standard value of 3.046, (3) time-varying equationof-state of dark energy described by the parameters w 0 and w a (alternatively quoted by the values at the T. M. C. ABBOTT et al. PHYS. REV. D 99, 123505 (2019) 123505-2 pivot redshift, w p , and w a), and (4) modified gravity described by the parameters μ 0 and Σ 0 that modify the metric potentials. We also consider external information from Planck cosmic microwave background measurements; baryon acoustic oscillation measurements from SDSS, 6dF, and BOSS; redshift-space distortion measurements from BOSS; and type Ia supernova information from the Pantheon compilation of datasets. Constraints on curvature and the number of relativistic species are dominated by the external data; when these are combined with DES Y1, we find Ω k ¼ 0.0020 þ0.0037 −0.0032 at the 68% confidence level, and the upper limit N eff < 3.28ð3.55Þ at 68% (95%) confidence, assuming a hard prior N eff > 3.0. For the timevarying equation-of-state, we find the pivot value ðw p ; w a Þ ¼ ð−0.91 þ0.19 −0.23 ; −0.57 þ0.93 −1.11 Þ at pivot redshift z p ¼ 0.27 from DES alone, and ðw p ; w a Þ ¼ ð−1.01 þ0.04 −0.04 ; −0.28 þ0.37 −0.48 Þ at z p ¼ 0.20 from DES Y1 combined with external data; in either case we find no evidence for the temporal variation of the equation of state. For modified gravity, we find the present-day value of the relevant parameters to be Σ 0 ¼ 0.43 þ0.28 −0.29 from DES Y1 alone, and ðΣ 0 ; μ 0 Þ ¼ ð0.06 þ0.08 −0.07 ; −0.11 þ0.42 −0.46 Þ from DES Y1 combined with external data. These modified-gravity constraints are consistent with predictions from general relativity.

Tension of the EG statistic and redshift space distortion data with the Planck - ΛCDM model and implications for weakening gravity

“…In the context of a fixed value of matter density determined by geometric probes of the cosmological background, the reduced gravitational growth could be either interpreted as a tension within the ΛCDM parameter value for the matter density or as a hint for weakening gravity. Indeed, such hints of weaker than expected gravitational growth of the Bardeen potentials has been observed at low redshifts by a wide range of dynamical probes including RSD observations [1,2,41,42], WL [25,34,[36][37][38][39] and CC data [29][30][31][32]. In most cases this weak growth has been interpreted as a tension for the parameters σ 8 and Ω 0m which are found by dynamical probes to be smaller than the values indicated by geometric probes in the context of ΛCDM .…”

Baryonic effects for weak lensing. Part II. Combination with X-ray data and extended cosmologies

“…Ref. [53], we impose w 0 + w a ≤ 0 as a hard prior. The other priors on the individual parameters are given in Table 1.…”

Investigating the degeneracy between modified gravity and massive neutrinos with redshift-space distortions

“…Indeed, it has been shown that the non-linear matter power spectrum [30] and halo mass function [35] in f (R) models are difficult to distinguish from their equivalents in GR when the neutrino masses are allowed to vary. The DES Collaboration considers neutrino mass and extensions to GR in the same analysis [36], although they only state the resulting constraints on the MG parameters and not the neutrino masses. There are some promising signs that certain observables may be better at reducing or even breaking this degeneracy, such as higher-order weak lensing statistics [37] and weak lensing tomographic information at multiple redshifts [38]; as well as techniques that are superior at distinguishing models such as machine learning [39,40].…”