We measure the correspondence between the distribution of galaxies and matter around troughs and peaks in the projected galaxy density, by comparing redMaGiC galaxies ( 0 . 15 < z < 0 . 65 ) t o weak lensing mass maps from the Dark Energy Survey (DES) Y3 data release. We obtain stacked profiles, as a function of angle θ, of the galaxy density contrast δg and the weak lensing convergence κ, in the vicinity of these identified troughs and peaks, referred to as ‘void’ and ‘cluster’ superstructures. The ratio of the profiles depend mildly on θ, indicating good consistency between the profile shapes. We model the amplitude of this ratio using a function F ( η, θ) that depends on cosmological parameters η, scaled by the galaxy bias. We construct templates of F ( η, θ) using a suite of N-body (Gower Street) simulations forward-modelled with DES Y3-like noise and systematics. We discuss and quantify the caveats of using a linear bias model to create galaxy maps from the simulation dark matter shells. We measure the galaxy bias in three lens tomographic bins (near to far): 2.32 ^+0.86 _−0.27 , 2 .18 ^+0.86 _−0.23 , 1.86 ^+0.82 _−0.23 for voids, and 2.46 ^+0.73 _−0.27 , 3.55 ^+0.96 _−0.55 , 4.27 ^+0.36 _−1.14 for clusters, assuming the best-fitting Planck cosmology. Similar values with ∼0.1 σshifts are obtained assuming the mean DES Y3 cosmology. The biases from troughs and peaks are broadly consistent, although a larger bias is derived for peaks, which is also larger than those measured from the DES Y3 3 ×2 -point analysis. This method shows an interesting avenue for measuring field-level bias that can be applied to future lensing surveys.

The Dark Energy Survey (DES) recently released the final results of its two principal probes of the expansion history: Type Ia supernovae (SNe) and baryonic acoustic oscillations (BAO). In this paper, we explore the cosmological implications of these data in combination with external cosmic microwave background (CMB), big bang nucleosynthesis (BBN), and age-of-the-Universe information. The BAO measurement, which is ∼2⁢𝜎 away from Planck ’s Λ⁢CDM predictions, pushes for low values of Ω_m compared to Planck, in contrast to SN which prefers a higher value than Planck. We identify several tensions among datasets in the Λ⁢CDM model that cannot be resolved by including either curvature (𝑘⁢Λ⁢CDM) or a constant dark energy equation of state (𝑤⁢CDM). By combining BAO+SN+CMB despite these mild tensions, we obtain in 𝑘⁢Λ⁢CDM, and in 𝑤⁢CDM. In 𝑤⁢CDM, BAO and SN push again in different directions of parameter space, favoring, respectively, 𝑤 <−1 and 𝑤 >−1. If we open the parameter space to 𝑤₀⁢𝑤_𝑎⁢CDM [where the equation of state of dark energy varies as 𝑤⁡(𝑎) =𝑤₀ +(1 −𝑎)⁢𝑤_𝑎], all the datasets are mutually more compatible, and we find concordance in the [𝑤₀ >−1,𝑤_𝑎 <0] quadrant, with BAO pushing for 𝑤_𝑎 <0 and SN for [𝑤₀ >−1,𝑤_𝑎 <0]. For DES BAO and SN in combination with Planck -CMB, we find a 3.2⁢𝜎 deviation from Λ⁢CDM, with , , a Hubble constant of , and an abundance of matter of . For the combination of all the background cosmological probes considered (including CMB’s angular acoustic scale 𝜃_⋆), we still find a deviation of 2.8⁢𝜎 from Λ⁢CDM in the 𝑤₀ −𝑤_𝑎 plane. Assuming a minimal neutrino mass, this work provides tentative evidence for non-Λ⁢CDM physics, which is consistent with recent claims in support of evolving dark energy, or a source of unknown systematics.

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the cosmogridv1 suite of 𝑁-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2–3 × higher figures of merit in the Ω𝑚−𝑆8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming stage-IV wide-field imaging surveys.

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and healpix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite – specifically designed to mimic DES Y3 data properties – we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under 0.3σ⁠) in the Ω_m − S₈ plane. Assuming the wCold Dark Matter model, our combined Betti numbers and second moments analysis yields S₈ = 0.821 ± 0.018 and Ω_m = 0.304 ± 0.037 – constraints 70 per cent tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

We describe the photometric dataset assembled from the full 6 yr of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated dataset derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value-added information. Y6 Gold comprises nearly 5000 deg² of grizY imaging in the south Galactic cap and includes 669 million objects with a depth of i_AB ∼ 23.4 mag at a signal-to-noise ratio ∼ 10 for extended objects and a top-of-the-atmosphere photometric uniformity <2 mmag. Y6 Gold augments DES DR2 with simultaneous fits to multiepoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star–galaxy classification with an efficiency of 98.6% and a contamination of 0.8% for galaxies with 17.5 < i_AB < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This publication is complemented by data access and documentation.

We present improved cosmological constraints from a re-analysis of the Dark Energy Survey (DES) 5-year sample of Type Ia supernovae (DES-SN5YR). This re-analysis includes an improved photometric cross-calibration, recent white dwarf observations to cross-calibrate between DES and low-redshift surveys, retraining the salt3 light-curve model and fixing a numerical approximation in the host-galaxy colour law. Our fully recalibrated sample, which we call DES-Dovekie, comprises (Formula presented) 1600 likely Type Ia SNe from DES and (Formula presented) 200 low-redshift SNe from other surveys. With DES-Dovekie, we obtain (Formula presented) in flat Lambda-cold dark matter ((Formula presented) CDM) which changes (Formula presented) by (Formula presented) compared to DES-SN5YR. Combining DES-Dovekie with cosmic microwave background data from Planck, Atacama Cosmology Telescope, and South Pole Telescope and the DESI DR2 measurements in a flat (Formula presented) CDM cosmology, we find (Formula presented) and (Formula presented). Our results hold a significance of (Formula presented), reduced from (Formula presented) for DES-SN5YR, to reject the null hypothesis that the data are compatible with the cosmological constant. This significance is equivalent to a Bayesian model preference odds of approximately 5:1 in favour of the flat (Formula presented) CDM model. Using generally accepted thresholds for model preference, our updated data exhibits only a weak preference for evolving dark energy.

This chapter provides a comprehensive overview of weak gravitational lensing and its current applications in cosmology. We begin by introducing the fundamental concepts of gravitational lensing and derive the key equations for the deflection angle, lensing potential, convergence, and shear. We explore how weak lensing can be used as a cosmological probe, discussing cosmic shear, galaxy–galaxy lensing, and their combination with galaxy clustering in the 3×2pt analysis. The chapter covers the theoretical framework for modeling lensing observables, shear estimation techniques, and major systematic effects such as intrinsic alignments and baryonic feedback. We review the current results of weak lensing cosmology from major surveys and outline prospects for future advancements in the field.

Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the Λ⁢CDM model. This work presents the first set of cosmological constraints derived with ∼16,000 optically selected redMaPPer clusters across nearly 5000  deg2 using DES year 3 datasets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3 ×2⁢pt), ensuring direct comparability with the DES-Y3 3 ×2⁢pt analysis. We obtain constraints  of 𝑆8 =0.864 ±0.035 and Ωm =0.26⁢5+0.019−0.031 from the cluster-based data vector. We find that cluster constraints and 3 ×2⁢pt constraints are consistent under the Λ⁢CDM model with a posterior predictive distribution (PPD) value of 0.53. The consistency between clusters and 3 ×2⁢pt provides a stringent test of Λ⁢CDM across different mass and spatial scales. Jointly analyzing clusters with 3 ×2⁢pt further improves cosmological constraints, yielding 𝑆8 =0.81⁢1+0.022−0.020 and Ωm =0.29⁢4+0.022−0.033, a 24% improvement in the Ωm −𝑆8 figure of merit over 3 ×2⁢pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of 0.6, significantly reducing previous 𝑆8 tension claims. Finally, combining DES 3 ×2⁢pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of ∑𝑚𝜈 <0.26  eV at 95% confidence under the Λ⁢CDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming stage-IV surveys such as LSST, Euclid, and Roman.

We present a cosmological analysis of the third-order aperture mass statistic using Dark Energy Survey Year 3 (DES Y3) data. We perform a complete tomographic measurement of the three-point correlation function of the Y3 weak lensing shape catalog with the four fiducial source redshift bins. Building upon our companion methodology paper, we apply a pipeline that combines the two-point function 𝜉± with the mass aperture skewness statistic ⟨⟩, which is an efficient compression of the full shear three-point function. We use a suite of simulated shear maps to obtain a joint covariance matrix. By jointly analyzing 𝜉± and ⟨⟩ measured from DES Y3 data with a Λ⁢CDM model, we find 𝑆₈=0.780±0.015 and Ω_m =, yielding 111% of figure-of-merit improvement in the Ω_𝑚−𝑆₈ plane relative to 𝜉± alone, consistent with expectations from simulated likelihood analyses. With a 𝑤⁢CDM model, we find 𝑆₈ = and 𝑤₀ =−1.39 ±0.31, which gives an improvement of 22% on the joint 𝑆₈−𝑤₀ constraint. Our results are consistent with 𝑤₀ =−1. Our new constraints are compared to CMB data from the Planck satellite, and we find that with the inclusion of ⟨⟩ the existing tension between the datasets is at the level of 2.3⁢𝜎. We show that the third-order statistic enables us to self-calibrate the mean photometric redshift uncertainty parameter of the highest redshift bin with little degradation in the figure of merit. Our results demonstrate the constraining power of higher-order lensing statistics and establish ⟨⟩ as a practical observable for joint analyses in current and future surveys.

We present the METADETECTION weak lensing galaxy shape catalogue from the 6-yr Dark Energy Survey (DES Y6) imaging data. This data set is the final release from DES, spanning 4422 deg² of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition, and shear measurements with METADETECTION. The DES Y6 METADETECTION weak lensing shape catalogue consists of 151 922 791 galaxies detected over riz bands, with an effective number density of n_eff = 8.22 galaxies per arcmin² and shape noise of σ_e = 0.29. We carry out a suite of validation tests on the catalogue, including testing for point spread function (PSF) leakage, testing for the impact of PSF modelling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-per cent level, with m = (3.4 ± 6.1) × 10⁻³, at 3σ uncertainty. This is the first time both cell-based coaddition and METADETECTION algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys.