118 Search Results

We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$$^{2}$$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 μK-arcmin at 95, 150, and 220 GHz, respectively. Candidates are selected via the thermal Sunyaev–Zel’dovich (SZ) effect with a minimum significance of ξ = 4.0, resulting in a catalog of purity ∼89%. Optical data from the Dark Energy Survey and infrared data from the Spitzer Space Telescope are used to confirm 442 cluster candidates. The clusters span 0.12 < z ≲ 1.8 and 1.0 × 10$$^{14}$$M$$_{⊙}$$/h$$_{70}$$ < M$$_{500c}$$ < 8.7 × 10$$^{14}$$M$$_{⊙}$$/h$$_{70}$$. The sample’s median redshift is 0.74, and themore » median mass is 1.7 × 10$$^{14}$$M$$_{⊙}$$/h$$_{70}$$; these are the lowest median mass and highest median redshift of any SZ-selected sample to date. We assess the effect of infrared emission from cluster member galaxies on cluster selection by performing a joint fit to the infrared dust and tSZ signals by combining measurements from SPT and overlapping submillimeter data from Herschel/SPIRE. We find that at high redshift (z > 1), the tSZ signal is reduced by 17.9−3.2+3.8%(3.8−0.7+0.9%) at 150 GHz (95 GHz) due to dust contamination. We repeat our cluster finding method on dust-nulled SPT maps and find the resulting catalog is consistent with the nominal SPT-Deep catalog, suggesting dust contamination does not significantly impact the SPT-Deep selection function; we attribute this lack of bias to the inclusion of the SPT 220 GHz band.« less

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified asmore » “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.« less

A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior p(q,n∣D)∝L(D∣q,n)p(q)p(n),where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n,  i.e., one needs to provide a density estimator over the full n space from the provided samples.more » But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter L.The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.« less

Observations of density variations in stellar streams are a promising probe of low-mass dark matter substructure in the Milky Way. However, survey systematics such as variations in seeing and sky brightness can also induce artificial fluctuations in the observed densities of known stellar streams. These variations arise because survey conditions affect both object detection and star–galaxy misclassification rates. To mitigate these effects, we use Balrog synthetic source injections in the Dark Energy Survey (DES) Y3 data to calculate detection rate variations and classification rates as functions of survey properties. We show that these rates are nearly separable with respect tomore » survey properties and can be estimated with sufficient statistics from the synthetic catalogs. Applying these corrections reduces the standard deviation of relative detection rates across the DES footprint by a factor of 5, and our corrections significantly change the inferred linear density of the Phoenix stream when including faint objects. Additionally, for artificial streams with DES-like survey properties we are able to recover density power spectra with reduced bias. We also find that uncorrected power-spectrum results for Legacy Survey of Space and Time (LSST)-like data can be around 5 times more biased, highlighting the need for such corrections in future ground-based surveys.« less

The properties of Milky Way satellite galaxies have important implications for galaxy formation, reionization, and the fundamental physics of dark matter. However, the population of Milky Way satellites includes the faintest known galaxies, and current observations are incomplete. To understand the impact of observational selection effects on the known satellite population, we perform rigorous, quantitative estimates of the Milky Way satellite galaxy detection efficiency in three wide-field survey datasets: the Dark Energy Survey Year 6, the DECam Local Volume Exploration Data Release 3, and the Pan-STARRS1 Data Release 1. Together, these surveys cover ∼13,600 deg² to g ∼ 24.0 andmore » ∼27,700 deg² to g ∼ 22.5, spanning ∼91% of the high-Galactic-latitude sky (∣b∣ ≥ 15°). We apply multiple detection algorithms over the combined footprint and recover 49 known satellites above a strict census detection threshold. To characterize the sensitivity of our census, we run our detection algorithms on a large set of simulated galaxies injected into the survey data, which allows us to develop models that predict the detectability of satellites as a function of their properties. We then fit an empirical model to our data and infer the luminosity function, radial distribution, and size–luminosity relation of Milky Way satellite galaxies. Our empirical model predicts a total of $$265^{+79}_{-47}$$ satellite galaxies with −20 ≤ M_V ≤ 0, half-light radii of 15 ≤ r_1/2, (pc) ≤ 3000, and galactocentric distances of 10 ≤ D_GC(kpc) ≤ 300. We also identify a mild anisotropy in the angular distribution of the observed galaxies, at a significance of ∼2σ, which can be attributed to the clustering of satellites associated with the LMC.« less

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 $\sim 2\sigma$ away from Planck ’s $\mathrm{\Lambda }\mathrm{CDM}$ predictions, pushes for low values of ${\mathrm{\Omega }}_{\mathrm{m}}$ compared to Planck, in contrast to SN which prefers a higher valuemore » than Planck. We identify several tensions among datasets in the $\mathrm{\Lambda }\mathrm{CDM}$ model that cannot be resolved by including either curvature ( $k\mathrm{\Lambda }\mathrm{CDM}$ ) or a constant dark energy equation of state ( $w\mathrm{CDM}$ ). By combining $\mathrm{BAO}+\mathrm{SN}+\mathrm{CMB}$ despite these mild tensions, we obtain ${\mathrm{\Omega }}_k=-{5.5}_{-4.2}^{+4.6}\times {10}^{-3}$ in $k\mathrm{\Lambda }\mathrm{CDM}$ , and $w=-0.948_{-0.027}^{+0.028}$ in $w\mathrm{CDM}$ . In  $w\mathrm{CDM}$ , 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 $w_0{w}_a\mathrm{CDM}$ [where the equation of state of dark energy varies as $w\left(a\right)=w_0+(1-a)w_a$ ], all the datasets are mutually more compatible, and we find concordance in the $[w_0>-1,w_a<0]$ quadrant, with BAO pushing for $w_a<0$ and SN for $[w_0>-1,w_a<0]$ . For DES BAO and SN in combination with Planck -CMB, we find a $3.2\sigma$ deviation from $\mathrm{\Lambda }\mathrm{CDM}$ , with $w_0=-0.673_{-0.097}^{+0.098}$ , $w_a=-1.37_{-0.50}^{+0.51}$ , a Hubble constant of $H_0=67.81_{-0.86}^{+0.96}\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$ , and an abundance of matter of ${\mathrm{\Omega }}_{\mathrm{m}}=0.3109_{-0.0099}^{+0.0086}$ . For the combination of all the background cosmological probes considered (including CMB’s angular acoustic scale ${\theta }_{\star }$ ), we still find a deviation of $2.8\sigma$ from $\mathrm{\Lambda }\mathrm{CDM}$ in the $w_0-w_a$ plane. Assuming a minimal neutrino mass, this work provides tentative evidence for non- $\mathrm{\Lambda }\mathrm{CDM}$ physics, which is consistent with recent claims in support of evolving dark energy, or a source of unknown systematics.« less

We report the discovery of three Milky Way satellite candidates: Carina IV, Phoenix III, and DELVE 7, in the third data release of the DECam Local Volume Exploration survey (DELVE). The candidate systems were identified by cross-matching results from two independent search algorithms. All three are extremely faint systems composed of old, metal-poor stellar populations (τ ≳ 10 Gyr, [Fe/H] ≲−1.4). Carina IV (M_V = −2.8; r_1/2 = 40 pc) and Phoenix III (M_V = −1.2; r_1/2 = 19 pc) have half-light radii that are consistent with the known population of dwarf galaxies, while DELVE 7 (M_V = 1.2; r_1/2more » = 2 pc) is very compact and seems more likely to be a star cluster, though its nature remains ambiguous without spectroscopic follow-up. The Gaia proper motions of stars in Carina IV ($$M_{\star} = 2250^{+1180}_{-830} M_⊙$$) indicate that it is unlikely to be associated with the LMC, while DECam CaHK photometry confirms that its member stars are metal poor. Phoenix III ($$M_{\star} = 520^{+660}_{-290} M_⊙$$) is the faintest known satellite in the extreme outer stellar halo (D_GC > 100 kpc), while DELVE 7 ($$M_{\star} = 60^{+120}_{-40} M_⊙$$) is the faintest known satellite with D_GC > 20 kpc.« less

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 deg$$^{2}$$ 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 multiepochmore » 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.« less

We present the results of a search for galaxy clusters in the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) microwave sky maps covering 16293 square degrees in three frequency bands, using data obtained over the lifetime of the project (2008-2022). We report redshifts and mass estimates for 10040 clusters detected via their Sunyaev-Zel'dovich (SZ) effect with signal-to-noise greater than 4 at a 2.4 arcminute filter scale. The catalog includes 1180 clusters at redshifts greater than 1, and 124 clusters at redshifts greater than 1.5. Using a relation between cluster SZ signal and mass that is consistent with recent weak-lensingmore » measurements, we estimate that clusters detected with signal-to-noise greater than 5 form a sample which is 90% complete for clusters with masses greater than $$5 \times 10^{14}$$ MSun (measured within a spherical volume with mean density 500 times the critical density). El Gordo, a cluster found in an initial ACT survey of 755 square degrees, remains the most extreme cluster in mass and redshift; we find no cluster with a mass and redshift combination high enough to falsify the standard LCDM cosmology with Gaussian initial perturbations. We make public a variety of data products, including the full cluster candidate list, noise maps, and sky masks, along with our software for cluster detection and instructions for reproducing our cluster catalogs from the public ACT maps.« less

We develop a framework to study the relation between the stellar mass of a galaxy and the total mass of its host dark matter halo using galaxy clustering and galaxy-galaxy lensing measurements. We model a wide range of scales, roughly from $$\sim 100 \; {\rm kpc}$$ to $$\sim 100 \; {\rm Mpc}$$, using a theoretical framework based on the Halo Occupation Distribution and data from Year 3 of the Dark Energy Survey (DES) dataset. The new advances of this work include: 1) the generation and validation of a new stellar mass-selected galaxy sample in the range of $$\log M_\star/M_\odot \simmore » 9.6$$ to $$\sim 11.5$$; 2) the joint-modeling framework of galaxy clustering and galaxy-galaxy lensing that is able to describe our stellar mass-selected sample deep into the 1-halo regime; and 3) stellar-to-halo mass relation (SHMR) constraints from this dataset. In general, our SHMR constraints agree well with existing literature with various weak lensing measurements. We constrain the free parameters in the SHMR functional form $$\log M_\star (M_h) = \log(εM_1) + f\left[ \log\left( M_h / M_1 \right) \right] - f(0)$$, with $$f(x) \equiv -\log(10^{αx}+1) + δ[\log(1+\exp(x))]^γ/ [1+\exp(10^{-x})]$$, to be $$\log M_1 = 11.506^{+0.325}_{-0.404}$$, $$\log ε= -1.632^{+0.306}_{-0.181}$$, $$α= -1.638^{+0.108}_{-0.099}$$, $$γ= 0.596^{+0.251}_{-0.210}$$ and $$δ= 3.810^{+2.045}_{-1.811}$$. The inferred average satellite fraction is within $$\sim 5-35\%$$ for our fiducial results and we do not see any clear trends with redshift or stellar mass. Furthermore, we find that the inferred average galaxy bias values follow the generally expected trends with stellar mass and redshift. Our study is the first SHMR in DES in this mass range, and we expect the stellar mass sample to be of general interest for other science cases.« less