106 Search Results

We present constraints on the normal branch of the Dvali-Gabadadze-Porrati (nDGP) braneworld gravity model from the abundance of massive galaxy clusters. On scales below the nDGP crossover scale $r_{\mathrm{c}}$ , the nDGP model features an effective gravitylike fifth force that alters the growth of structure, leading to an enhancement of the halo mass function (HMF) on cluster scales. The enhanced cluster abundance allows for constraints on the nDGP model using cluster samples. We employ the South Pole Telescope (SPT) cluster sample, selected through the thermal Sunyaev-Zel’dovich effect with the SPT and with mass calibrationmore » using weak-lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample contains 1,005 clusters with redshifts $0.25<z<1.78$ , which are confirmed with the multicomponent matched filter algorithm using optical and near-infrared data. Weak-lensing data from DES and HST enable a robust mass measurement of the cluster sample. We use DES Year 3 data for 688 clusters with redshifts $z<0.95$ , and HST data for 39 clusters with redshifts $0.6<z<1.7$ . We account for the enhancement in the HMF through a semi-analytic correction factor to the standard cosmology HMF derived from the spherical collapse model in the nDGP model. We then further calibrate this model using $N$ -body simulations. In addition, for the first time, we analyze the primary CMB temperature and polarization anisotropy measurements from Planck PR4 within the nDGP model. We obtain a competitive constraint from the joint analysis of the SPT cluster abundance with the Planck PR4 data, and report an upper bound of $1/\sqrt{H_0{r}_{\mathrm{c}}}<1.41$ at 95% when assuming a cosmology with massive neutrinos.« less

We present a measurement of the angular baryon acoustic oscillation (BAO) scale from the completed Dark Energy Survey (DES) dataset excluding the area of overlap with the Dark Energy Spectroscopic Instrument (DESI). We follow the same methodology and validation process as in the DES Y6 BAO analysis. We interpret the impact of this measurement in the context of the statistical preference for “ $w_0{w}_a$ cold dark matter (CDM) over $\mathrm{\Lambda }\mathrm{CDM}$ when combined with DES Y5 Type Ia supernovae (SN), Planck CMB, and DESI BAO. Based on our previous work,more » using the full Y6 DES BAO sample, in combination with SN, CMB and DESI data release 1 (DR1) BAO, added $0.3\sigma$ in this preference (from $3.7\sigma$ to $4.0\sigma$ ), but this ignored possible correlations between datasets. Using our new DESI-independent DES BAO likelihood instead, we find a smaller increase in the statistical preference for $w_0{w}_a\mathrm{CDM}$ , from $3.7\sigma$ to $3.8\sigma$ when using DESI DR1 BAO, and from $4.0\sigma$ to $4.1\sigma$ when updating to the more recent DESI data release 2 (DR2) BAO. These significances reduce to $3.1\sigma$ when using the new calibrated DES SN-Dovekie. Alongside this work, we publicly release baofit_wtheta, the BAO fitting code for the angular correlation function used in the DES Y6 BAO analysis.« 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

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 $$\sim$$1600 likely Type Ia SNe from DES and $$\sim$$200 low-redshift SNe from other surveys. With DES-Dovekie, we obtain $$Ω_{\rm m} = 0.330 \pm 0.015$$ in Flat $$Λ$$CDM which changes $$Ω_{\rm m}$$ by $-0.022$ comparedmore » to DES-SN5YR. Combining DES-Dovekie with CMB data from Planck, ACT and SPT and the DESI DR2 measurements in a Flat $$w_0 w_a$$CDM cosmology, we find $$w_0 = -0.803 \pm 0.054$$, $$w_a = -0.72 \pm 0.21$$. Our results hold a significance of $3.2σ$, reduced from $4.2σ$ 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 $$w_0 w_a$$CDM model. Using generally accepted thresholds for model preference, our updated data exhibits only a weak preference for evolving dark energy.« 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

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

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

Aims. We investigate the utility of Rubin’s Data Preview 1 (DP1) for estimating stellar number density profiles across the Milky Way halo. Methods. We used stellar broad-band near-UV to near-IR ugrizy photometry released in Rubin’s DP1 to estimate distance and metallicity for blue main sequence stars brighter than r = 24 in three ~1.1 sq. deg. fields at southern Galactic latitudes. Results. Compared to TRILEGAL simulations of the Galaxy’s stellar content, we found a likely deficit of blue main sequence turn-off stars with 22 < r < 24. We interpreted this discrepancy as a signature of a steeper halo numbermore » density profile at galactocentric distances 10–50 kpc than the canonical ~1/r³ profile assumed in TRILEGAL simulations. Conclusions. This interpretation is consistent with earlier suggestions based on observations of more luminous, but much less numerous, evolved stellar populations, along with a few pencil beam surveys of blue main sequence stars in the northern sky. These results bode well for the future Galactic halo exploration with Rubin’s Legacy Survey of Space and Time (LSST).« 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

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, 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 onmore » 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 𝛺_𝑚 − 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.« less