We present cosmological results from a combined analysis of galaxy clustering and weak gravitational lensing, using 1321 deg 2 of griz imaging data from the first year of the Dark Energy Survey (DES Y1). We combine three two-point functions: (i) the cosmic shear correlation function of 26 million source galaxies in four redshift bins, (ii) the galaxy angular autocorrelation function of 650,000 luminous red galaxies in five redshift bins, and (iii) the galaxy-shear cross-correlation of luminous red galaxy positions and source galaxy shears. To demonstrate the robustness of these results, we use independent pairs of galaxy shape, photometric-redshift estimation and validation, and likelihood analysis pipelines. To prevent confirmation bias, the bulk of the analysis was carried out while "blind" to the true results; we describe an extensive suite of systematics checks performed and passed during this blinded phase. The data are modeled in flat ΛCDM and wCDM cosmologies, marginalizing over 20 nuisance parameters, varying 6 (for ΛCDM) or 7 (for wCDM) cosmological parameters including the neutrino mass density and including the 457 × 457 element analytic covariance matrix. We find consistent cosmological results from these three two-point functions and from their combination obtain S 8 ≡ σ 8 ðΩ m =0.3Þ 0.5 ¼ 0.773 þ0.026 −0.020 and Ω m ¼ 0.267 þ0.030 −0.017 for ΛCDM; for wCDM, we find S 8 ¼ 0.782 þ0.036 −0.024 , Ω m ¼ 0.284 þ0.033 −0.030 , and w ¼ −0.82 þ0.21 −0.20 at 68% C.L. The precision of these DES Y1 constraints rivals that from the Planck cosmic microwave background measurements, allowing a comparison of structure in the very early and late Universe on equal terms. Although the DES Y1 best-fit values for S 8 and Ω m are lower than the central values from Planck for both ΛCDM and wCDM, the Bayes factor indicates that the DES Y1 and Planck data sets are consistent with each other in the context of ΛCDM. Combining DES Y1 with Planck, baryonic acoustic oscillation measurements from SDSS, 6dF, and BOSS and type Ia supernovae from the Joint Lightcurve Analysis data set, we derive very tight constraints on cosmological parameters: S 8 ¼ 0.802 AE 0.012 and Ω m ¼ 0.298 AE 0.007 in ΛCDM and w ¼ −1.00 þ0.05 −0.04 in wCDM. Upcoming Dark Energy Survey analyses will provide more stringent tests of the ΛCDM model and extensions such as a time-varying equation of state of dark energy or modified gravity.

The pseudo-C is an algorithm for estimating the angular power and cross-power spectra that is very fast and in realistic cases also nearly optimal. The algorithm can be extended to deal with contaminant deprojection and E/B purification, and can therefore be applied in a wide variety of scenarios of interest for current and future cosmological observations. This paper presents NaMaster, a public, validated, accurate and easy-to-use software package that, for the first time, provides a unified framework to compute angular cross-power spectra of any pair of spin-0 or spin-2 fields, contaminated by an arbitrary number of linear systematics and requiring B-or E-mode purification, both on the sphere or in the flat-sky approximation. We describe the mathematical background of the estimator, including all the features above, and its software implementation in NaMaster. We construct a validation suite that aims to resemble the types of observations that next-generation large-scale structure and ground-based CMB experiments will face, and use it to show that the code is able to recover the input power spectra in the most complex scenarios with no detectable bias. NaMaster can be found at https://github.com/LSSTDESC/NaMaster, and is provided with comprehensive documentation and a number of code examples.

This work and its companion paper, Amon et al. [Phys. Rev. D 105, 023514 (2022)], present cosmic shear measurements and cosmological constraints from over 100 million source galaxies in the Dark Energy Survey (DES) Year 3 data. We constrain the lensing amplitude parameter S 8 ≡ σ 8 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ω m =0.3 p at the 3% level in ΛCDM: S 8 ¼ 0.759 þ0.025 −0.023 (68% CL). Our constraint is at the 2% level when using angular scale cuts that are optimized for the ΛCDM analysis: S 8 ¼ 0.772 þ0.018 −0.017 (68% CL). With cosmic shear alone, we †

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This work, together with its companion paper, Secco, Samuroff et al. [Phys. Rev. D 105, 023515 (2022)], present the Dark Energy Survey Year 3 cosmic-shear measurements and cosmological constraints based on an analysis of over 100 million source galaxies. With the data spanning 4143 deg 2 on the sky, divided into four redshift bins, we produce a measurement with a signal-to-noise of 40. We conduct a blind analysis in the context of the Lambda-Cold Dark Matter (ΛCDM) model and find a 3% constraint of the clustering amplitude, S 8 ≡ σ 8 ðΩ m =0.3Þ 0.5 ¼ 0.759 þ0.025 −0.023 . A ΛCDM-Optimized analysis, which safely includes smaller scale information, yields a 2% precision measurement of S 8 ¼ 0.772 þ0.018 −0.017 that is consistent with the fiducial case. The two low-redshift measurements are statistically consistent with the Planck Cosmic Microwave Background result, however, both recovered S 8 values are lower than the highredshift prediction by 2.3σ and 2.1σ (p-values of 0.02 and 0.05), respectively. The measurements are shown to be internally consistent across redshift bins, angular scales and correlation functions. The analysis is demonstrated to be robust to calibration systematics, with the S 8 posterior consistent when varying the choice of redshift calibration sample, the modeling of redshift uncertainty and methodology. Similarly, we find that the corrections included to account for the blending of galaxies shifts our best-fit S 8 by 0.5σ without incurring a substantial increase in uncertainty. We examine the limiting factors for the precision of the cosmological constraints and find observational systematics to be subdominant to the modeling of astrophysics. Specifically, we identify the uncertainties in modeling baryonic effects and intrinsic alignments as the limiting systematics.

We study the clustering of galaxies detected at i < 22.5 in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using 2.3 × 10 6 galaxies over a contiguous 116 deg 2 region in five bins of photometric redshift width ∆z = 0.2 in the range 0.2 < z < 1.2. The impact of photometric redshift errors are assessed by comparing results using a template-based photo-z algorithm (BPZ) to a machine-learning algorithm (TPZ). A companion paper (Leistedt et al 2015) presents maps of several observational variables (e.g. seeing, sky brightness) which could modulate the galaxy density. Here we characterize and mitigate systematic errors on the measured clustering which arise from these observational variables, in addition to others such as Galactic dust and stellar contamination. After correcting for systematic effects we measure galaxy bias over a broad range of linear scales relative to mass clustering predicted from the Planck ΛCDM model, finding agreement with CFHTLS measurements with χ 2 of 4.0 (8.7) with 5 degrees of freedom for the TPZ (BPZ) redshifts. We test a "linear bias" model, in which the galaxy clustering is a fixed multiple of the predicted non-linear dark-matter clustering. The precision of the data allow us to determine that the linear bias model describes the observed galaxy clustering to 2.5% accuracy down to scales at least 4 to 10 times smaller than those on which linear theory is expected to be sufficient.

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.

This paper reviews recent progress in the development of syndromic surveillance systems for veterinary medicine. Peer-reviewed and grey literature were searched in order to identify surveillance systems that explicitly address outbreak detection based on systematic monitoring of animal population data, in any phase of implementation. The review found that developments in veterinary syndromic surveillance are focused not only on animal health, but also on the use of animals as sentinels for public health, representing a further step towards One Medicine. The main sources of information are clinical data from practitioners and laboratory data, but a number of other sources are being explored. Due to limitations inherent in the way data on animal health is collected, the development of veterinary syndromic surveillance initially focused on animal health data collection strategies, analyzing historical data for their potential to support systematic monitoring, or solving problems of data classification and integration. Systems based on passive notification or data transfers are now dealing with sustainability issues. Given the ongoing barriers in availability of data, diagnostic laboratories appear to provide the most readily available data sources for syndromic surveillance in animal health. As the bottlenecks around data source availability are overcome, the next challenge is consolidating data standards for data classification, promoting the integration of different animal health surveillance systems, and also the integration to public health surveillance. Moreover, the outputs of systems for systematic monitoring of animal health data must be directly connected to real-time decision support systems which are increasingly being used for disease management and control.