We describe the first public data release of the Dark Energy Survey, DES DR1, consisting of reduced single-epoch images, co-added images, co-added source catalogs, and associated products and services assembled over the first 3 yr of DES science operations. DES DR1 is based on optical/near-infrared imaging from 345 distinct nights (2013 August to 2016 February) by the Dark Energy Camera mounted on the 4 m Blanco telescope at the Cerro Tololo Inter-American Observatory in Chile. We release data from the DES wide-area survey covering ∼5000 deg 2 of the southern Galactic cap in five broad photometric bands, grizY. DES DR1 has a median delivered point-spread function of = g 1.12, r=0.96, i=0.88, z=0.84, and Y=0 90 FWHM, a photometric precision of <1% in all bands, and an astrometric precision of 151 mas. The median co-added catalog depth for a 1 95 diameter aperture at signal-to-noise ratio (S/N)=10 is g=24.33, r=24.08, i=23.44, z=22.69, and Y=21.44 mag. DES DR1 includes nearly 400 million distinct astronomical objects detected in ∼10,000 co-add tiles of size 0.534 deg 2 produced from ∼39,000 individual exposures. Benchmark galaxy and stellar samples contain ∼310 million and ∼80 million objects, respectively, following a basic object quality selection. These data are accessible through a range of interfaces, including query web clients, image cutout servers, jupyter notebooks, and an interactive co-add image visualization tool. DES DR1 constitutes the largest photometric data set to date at the achieved depth and photometric precision.

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX.

An intriguing discrepancy emerging in the concordance model of cosmology is the tension between the locally measured value of the Hubble rate, and the 'global' value inferred from the cosmic microwave background (CMB). This could be due to systematic uncertainties when measuring H 0 locally, or it could be that we live in a highly unlikely Hubble bubble, or other exotic scenarios. We point out that the global H 0 can be found by extrapolating H(z) data points at high-z down to z = 0. By doing this in a Bayesian non-parametric way we can find a model-independent value for H 0 . We apply this to 19 measurements based on differential age of passively evolving galaxies as cosmic chronometers. Using Gaussian processes, we find H 0 = 64.9 ± 4.2 km s −1 Mpc −1 (1σ), in agreement with the CMB value, but reinforcing the tension with the local value. An analysis of possible sources of systematic errors shows that the stellar population synthesis model adopted may change the results significantly, being the main concern for subsequent studies. Forecasts for future data show that distant H(z) measurements can be a robust method to determine H 0 , where a focus in precision and a careful assessment of systematic errors are required.

Model-independent methods in cosmology has become an essential tool in order to deal with an increasing number of theoretical alternatives for explaining the late-time acceleration of the Universe. In principle, this provides a way of testing the Cosmological Concordance (or ΛCDM) model under different assumptions and ruling out whole classes of competing theories. One such modelindependent method is the so-called cosmographic approach, which relies only on the homogeneity and isotropy of the Universe on large scales. We show that this method suffers from many shortcomings, providing biased results depending on the auxiliary variable used in the series expansion and is unable to rule out models or adequately reconstruct theories with higher-order derivatives in either the gravitational or matter sector. Consequently, in its present form, this method seems unable to provide reliable or useful results for cosmological applications.