The latest Planck results reconfirm the existence of a slight but chronic tension between the best-fit Cosmic Microwave Background (CMB) and low-redshift observables: power seems to be consistently lacking in the late universe across a range of observables (e.g.~weak lensing, cluster counts). We propose a two-parameter model for dark energy where the dark energy is sufficiently like dark matter at large scales to keep the CMB unchanged but where it does not cluster at small scales, preventing concordance collapse and erasing power. We thus exploit the generic scale-dependence of dark energy instead of the more usual time-dependence to address the tension in the data. The combination of CMB, distance and weak lensing data somewhat prefer our model to $Lambda$CDM, at $Deltachi^2=2.4$. Moreover, this improved solution has $sigma_8=0.79 pm 0.02$, consistent with the value implied by cluster counts.
We argue that there is an intrinsic noise on measurements of the equation of state parameter $w=p/rho$ from large-scale structure around us. The presence of the large-scale structure leads to an ambiguity in the definition of the background universe and thus there is a maximal precision with which we can determine the equation of state of dark energy. To study the uncertainty due to local structure, we model density perturbations stemming from a standard inflationary power spectrum by means of the exact Lema^{i}tre-Tolman-Bondi solution of Einsteins equation, and show that the usual distribution of matter inhomogeneities in a $Lambda$CDM cosmology causes a variation of $w$ -- as inferred from distance measures -- of several percent. As we observe only one universe, or equivalently because of the cosmic variance, this uncertainty is systematic in nature.
The aim of this paper is to answer the following two questions: (1) Given cosmological observations of the expansion history and linear perturbations in a range of redshifts and scales as precise as is required, which of the properties of dark energy could actually be reconstructed without imposing any parameterization? (2) Are these observables sufficient to rule out not just a particular dark energy model, but the entire general class of viable models comprising a single scalar field? This paper bears both good and bad news. On one hand, we find that the goal of reconstructing dark energy models is fundamentally limited by the unobservability of the present values of the matter density Omega_m0, the perturbation normalization sigma_8 as well as the present matter power spectrum. On the other, we find that, under certain conditions, cosmological observations can nonetheless rule out the entire class of the most general single scalar-field models, i.e. those based on the Horndeski Lagrangian.
Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10^4 supernovae, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric supernova cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples which have been cut using typical selection criteria. The latter typically are either biased due to contamination or have significantly larger contours in the cosmological parameters due to small data-sets. We then apply BEAMS to the 792 SDSS-II photometric supernovae with host spectroscopic redshifts. In this case, BEAMS reduces the area of the (Omega_m,Omega_Lambda) contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 supernovae). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are Omega_m(BEAMS)=0.194pm0.07. This illustrates the potential power of BEAMS for future large photometric supernova surveys such as LSST.
