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.
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Using the N-body simulations of the AEMULUS Project, we construct an emulator for the non-linear clustering of galaxies in real and redshift space. We construct our model of galaxy bias using the halo occupation framework, accounting for possible velocity bias. The model includes 15 parameters, including both cosmological and galaxy bias parameters. We demonstrate that our emulator achieves ∼ 1% precision at the scales of interest, 0.1 < r < 10 h −1 Mpc, and recovers the true cosmology when tested against independent simulations. Our primary parameters of interest are related to the growth rate of structure, f , and its degenerate combination f σ 8 . Using this emulator, we show that the constraining power on these parameters monotonically increases as smaller scales are included in the analysis, all the way down to 0.1 h −1 Mpc. For a BOSS-like survey, the constraints on f σ 8 from r < 30 h −1 Mpc scales alone are more than a factor of two tighter than those from the fiducial BOSS analysis of redshift-space clustering using perturbation theory at larger scales. The combination of real-and redshift-space clustering allows us to break the degeneracy between f and σ 8 , yielding a 9% constraint on f alone for a BOSS-like analysis. The current AEMULUS simulations limit this model to surveys of massive galaxies. Future simulations will allow this framework to be extended to all galaxy target types, including emission-line galaxies.
We use weak-lensing shear measurements to determine the mean mass of optically selected galaxy clusters in Dark Energy Survey Science Verification data. In a blinded analysis, we split the sample of more than 8,000 redMaPPer clusters into 15 subsets, spanning ranges in the richness parameter 5 λ 180 and redshift 0.2 z 0.8, and fit the averaged mass density contrast profiles with a model that accounts for seven distinct sources of systematic uncertainty: shear measurement and photometric redshift errors; cluster-member contamination; miscentering; deviations from the NFW halo profile; halo triaxiality; and line-of-sight projections. We combine the inferred cluster masses to estimate the joint scaling relation between mass, richness and redshift, M(λ, z) ∝ M 0 λ F (1 + z) G . We find M 0 ≡ M 200m | λ = 30, z = 0.5 = 2.35 ± 0.22 (stat) ± 0.12 (sys) · 10 14 M , with F = 1.12 ± 0.20 (stat) ± 0.06 (sys) and G = 0.18 ± 0.75 (stat) ± 0.24 (sys). The amplitude of the mass-richness relation is in excellent agreement with the weak-lensing calibration of redMaPPer clusters in SDSS by Simet et al. (2017a) and with the Saro et al. (2015) calibration based on abundance matching of SPT-detected clusters. Our results extend the redshift range over which the mass-richness relation of redMaPPer clusters has been calibrated with weak lensing from z 0.3 to z 0.8. Calibration uncertainties of shear measurements and photometric redshift estimates dominate our systematic error budget and require substantial improvements for forthcoming studies.
The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. Predictions are provided for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias and the halo mass function through state-of-the-art modeling prescriptions available in the literature. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons between CCL and independent software packages, allows us to establish a well-defined numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is an open source software package written in C, with a python interface and publicly available at https://github.com/LSSTDESC/CCL.
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