The Dark Energy Spectroscopic Instrument (DESI) is carrying out a fiveyear survey that aims to measure the redshifts of tens of millions of galaxies and quasars, including 8 million luminous red galaxies (LRGs) in the redshift range 0.4 <
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Abstract z ≲ 1.0. Here we present the selection of the DESI LRG sample and assess its spectroscopic performance using data from Survey Validation (SV) and the first two months of the Main Survey. The DESI LRG sample, selected usingg ,r ,z , andW 1 photometry from the DESI Legacy Imaging Surveys, is highly robust against imaging systematics. The sample has a target density of 605 deg^{−2}and a comoving number density of 5 × 10^{−4}h ^{3}Mpc^{−3}in 0.4 <z < 0.8; this is a significantly higher density than previous LRG surveys (such as SDSS, BOSS, and eBOSS) while also extending toz ∼ 1. After applying a bright star veto mask developed for the sample, 98.9% of the observed LRG targets yield confident redshifts (with a catastrophic failure rate of 0.2% in the confident redshifts), and only 0.5% of the LRG targets are stellar contamination. The LRG redshift efficiency varies with source brightness and effective exposure time, and we present a simple model that accurately characterizes this dependence. In the appendices, wemore » 
ABSTRACT We evaluate the consistency between lensing and clustering based on measurements from Baryon Oscillation Spectroscopic Survey combined with galaxy–galaxy lensing from Dark Energy Survey (DES) Year 3, Hyper SuprimeCam Subaru Strategic Program (HSC) Year 1, and KiloDegree Survey (KiDS)1000. We find good agreement between these lensing data sets. We model the observations using the Dark Emulator and fit the data at two fixed cosmologies: Planck (S8 = 0.83), and a Lensing cosmology (S8 = 0.76). For a joint analysis limited to large scales, we find that both cosmologies provide an acceptable fit to the data. Full utilization of the higher signaltonoise smallscale measurements is hindered by uncertainty in the impact of baryon feedback and assembly bias, which we account for with a reasoned theoretical error budget. We incorporate a systematic inconsistency parameter for each redshift bin, A, that decouples the lensing and clustering. With a wide range of scales, we find different results for the consistency between the two cosmologies. Limiting the analysis to the bins for which the impact of the lens sample selection is expected to be minimal, for the Lensing cosmology, the measurements are consistent with A = 1; A = 0.91 ± 0.04 (A = 0.97 ± 0.06) using DES+KiDS (HSC). For the Planck case,more »

Abstract We use a recent census of the Milky Way (MW) satellite galaxy population to constrain the lifetime of particle dark matter (DM). We consider twobody decaying dark matter (DDM) in which a heavy DM particle decays with lifetime
τ comparable to the age of the universe to a lighter DM particle (with mass splittingϵ ) and to a dark radiation species. These decays impart a characteristic “kick velocity,”V _{kick}=ϵ c , on the DM daughter particles, significantly depleting the DM content of lowmass subhalos and making them more susceptible to tidal disruption. We fit the suppression of the presentday DDM subhalo mass function (SHMF) as a function ofτ andV _{kick}using a suite of highresolution zoomin simulations of MWmass halos, and we validate this model on new DDM simulations of systems specifically chosen to resemble the MW. We implement our DDM SHMF predictions in a forward model that incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk using an empirical model for the galaxy–halo connection. By comparing to the observed MW satellite population, we conservatively exclude DDM models withτ < 18 Gyrmore » 
ABSTRACT We present cosmological constraints from the analysis of angular power spectra of cosmic shear maps based on data from the first three years of observations by the Dark Energy Survey (DES Y3). Our measurements are based on the pseudoCℓ method and complement the analysis of the twopoint correlation functions in real space, as the two estimators are known to compress and select Gaussian information in different ways, due to scale cuts. They may also be differently affected by systematic effects and theoretical uncertainties, making this analysis an important crosscheck. Using the same fiducial Lambda cold dark matter model as in the DES Y3 realspace analysis, we find ${S_8 \equiv \sigma _8 \sqrt{\Omega _{\rm m}/0.3} = 0.793^{+0.038}_{0.025}}$, which further improves to S8 = 0.784 ± 0.026 when including shear ratios. This result is within expected statistical fluctuations from the realspace constraint, and in agreement with DES Y3 analyses of nonGaussian statistics, but favours a slightly higher value of S8, which reduces the tension with the Planck 2018 constraints from 2.3σ in the real space analysis to 1.5σ here. We explore less conservative intrinsic alignments models than the one adopted in our fiducial analysis, finding no clear preference for a more complex model. We also include smallmore »Free, publiclyaccessible full text available July 27, 2023

Free, publiclyaccessible full text available June 1, 2023

ABSTRACT Cosmological information from weak lensing surveys is maximized by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We present hyperrank, a new method for marginalizing over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parametrized models. In hyperrank, the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled, along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalizing over discrete realizations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency. We focus on the case of weak lensing cosmic shear analyses and demonstrate our method using simulations made for the Dark Energy Survey (DES). We show that the method can correctly and efficiently marginalize over a wide range of models for the redshift distribution uncertainty. Finally, we compare hyperrank to the common meanshifting method of marginalizing over redshift uncertainty, validating thatmore »Free, publiclyaccessible full text available February 11, 2023

ABSTRACT In this work, we present the galaxy clustering measurements of the two DES lens galaxy samples: a magnitudelimited sample optimized for the measurement of cosmological parameters, maglim, and a sample of luminous red galaxies selected with the redmagic algorithm. maglim/redmagic sample contains over 10 million/2.5 million galaxies and is divided into six/five photometric redshift bins spanning the range z ∈ [0.20, 1.05]/z ∈ [0.15, 0.90]. Both samples cover 4143 $\deg ^2$ over which we perform our analysis blind, measuring the angular correlation function with an S/N ∼ 63 for both samples. In a companion paper, these measurements of galaxy clustering are combined with the correlation functions of cosmic shear and galaxy–galaxy lensing of each sample to place cosmological constraints with a 3 × 2pt analysis. We conduct a thorough study of the mitigation of systematic effects caused by the spatially varying survey properties and we correct the measurements to remove artificial clustering signals. We employ several decontamination methods with different configurations to ensure the robustness of our corrections and to determine the systematic uncertainty that needs to be considered for the final cosmology analyses. We validate our fiducial methodology using lognormal mocks, showing that our decontamination procedure induces biases no greatermore »

Free, publiclyaccessible full text available April 1, 2023

Free, publiclyaccessible full text available April 1, 2023