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ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads. 

Abstract We present a search for outer solar system objects in the 6 yr of data from the Dark Energy Survey (DES). The DES covered a contiguous 5000 deg 2 of the southern sky with ≈80,000 3 deg 2 exposures in the grizY filters between 2013 and 2019. This search yielded 812 trans-Neptunian objects (TNOs), one Centaur and one Oort cloud comet, 458 reported here for the first time. We present methodology that builds upon our previous search on the first 4 yr of data. All images were reprocessed with an optimized detection pipeline that leads to an average completeness gain of 0.47 mag per exposure, as well as improved transient catalog production and algorithms for linkage of detections into orbits. All objects were verified by visual inspection and by the “sub-threshold significance,” the signal-to-noise ratio in the stack of images in which its presence is indicated by the orbit, but no detection was reported. This yields a pure catalog complete to r ≈ 23.8 mag and distances 29 < d < 2500 au. The TNOs have minimum (median) of 7 (12) nights’ detections and arcs of 1.1 (4.2) yr, and will have grizY magnitudes available in a further publication. We present software for simulating our observational biases for comparisons of models to our detections. Initial inferences demonstrating the catalog’s statistical power are: the data are inconsistent with the CFEPS-L7 model for the classical Kuiper Belt; the 16 “extreme” TNOs ( a > 150 au, q > 30 au) are consistent with the null hypothesis of azimuthal isotropy; and nonresonant TNOs with q > 38 au, a > 50 au show a significant tendency to be sunward of major mean-motion resonances. 

ABSTRACT As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNovatrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time. 

Abstract The Jupiter Trojans are a large group of asteroids that are coorbiting with Jupiter near its L4 and L5 Lagrange points. The study of Jupiter Trojans is crucial for testing different models of planet formation that are directly related to our understanding of solar system evolution. In this work, we select known Jupiter Trojans listed by the Minor Planet Center from the full six years data set (Y6) of the Dark Energy Survey (DES) to analyze their photometric properties. The DES data allow us to study Jupiter Trojans with a fainter magnitude limit than previous studies in a homogeneous survey withgrizband measurements. We extract a final catalog of 573 unique Jupiter Trojans. Our sample include 547 asteroids belonging to L5. This is one of the largest analyzed samples for this group. By comparing with the data reported by other surveys we found that the color distribution of L5 Trojans is similar to that of L4 Trojans. We find that L5 Trojans’g−iandg−rcolors become less red with fainter absolute magnitudes, a trend also seen in L4 Trojans. Both the L4 and L5 clouds consistently show such a color–size correlation over an absolute magnitude range 11 <H< 18. We also use DES colors to perform taxonomic classifications. C- and P-type asteroids outnumber D-type asteroids in the L5 Trojans DES sample, which have diameters in the 5–20 km range. This is consistent with the color–size correlation. 

ABSTRACT The CMB lensing signal from cosmic voids and superclusters probes the growth of structure in the low-redshift cosmic web. In this analysis, we cross-correlated the Planck CMB lensing map with voids detected in the Dark Energy Survey Year 3 (Y3) data set (∼5000 deg2), expanding on previous measurements that used Y1 catalogues (∼1300 deg2). Given the increased statistical power compared to Y1 data, we report a 6.6σ detection of negative CMB convergence (κ) imprints using approximately 3600 voids detected from a redMaGiC luminous red galaxy sample. However, the measured signal is lower than expected from the MICE N-body simulation that is based on the ΛCDM model (parameters Ωm = 0.25, σ8 = 0.8), and the discrepancy is associated mostly with the void centre region. Considering the full void lensing profile, we fit an amplitude $$A_{\kappa }=\kappa _{{\rm DES}}/\kappa _{{\rm MICE}}$$ to a simulation-based template with fixed shape and found a moderate 2σ deviation in the signal with Aκ ≈ 0.79 ± 0.12. We also examined the WebSky simulation that is based on a Planck 2018 ΛCDM cosmology, but the results were even less consistent given the slightly higher matter density fluctuations than in MICE. We then identified superclusters in the DES and the MICE catalogues, and detected their imprints at the 8.4σ level; again with a lower-than-expected Aκ = 0.84 ± 0.10 amplitude. The combination of voids and superclusters yields a 10.3σ detection with an Aκ = 0.82 ± 0.08 constraint on the CMB lensing amplitude, thus the overall signal is 2.3σ weaker than expected from MICE. 

ABSTRACT Reverberation mapping is a robust method to measure the masses of supermassive black holes outside of the local Universe. Measurements of the radius–luminosity (R−L) relation using the Mg ii emission line are critical for determining these masses near the peak of quasar activity at z ≈ 1−2, and for calibrating secondary mass estimators based on Mg ii that can be applied to large samples with only single-epoch spectroscopy. We present the first nine Mg ii lags from our 5-yr Australian Dark Energy Survey reverberation mapping programme, which substantially improves the number and quality of Mg ii lag measurements. As the Mg ii feature is somewhat blended with iron emission, we model and subtract both the continuum and iron contamination from the multiepoch spectra before analysing the Mg ii line. We also develop a new method of quantifying correlated spectroscopic calibration errors based on our numerous, contemporaneous observations of F-stars. The lag measurements for seven of our nine sources are consistent with both the H β and Mg ii R−L relations reported by previous studies. Our simulations verify the lag reliability of our nine measurements, and we estimate that the median false positive rate of the lag measurements is $$4{{\ \rm per\ cent}}$$. 

ABSTRACT We develop a novel data-driven method for generating synthetic optical observations of galaxy clusters. In cluster weak lensing, the interplay between analysis choices and systematic effects related to source galaxy selection, shape measurement, and photometric redshift estimation can be best characterized in end-to-end tests going from mock observations to recovered cluster masses. To create such test scenarios, we measure and model the photometric properties of galaxy clusters and their sky environments from the Dark Energy Survey Year 3 (DES Y3) data in two bins of cluster richness $$\lambda \in [30; 45)$$, $$\lambda \in [45; 60)$$ and three bins in cluster redshift ($$z\in [0.3; 0.35)$$, $$z\in [0.45; 0.5)$$ and $$z\in [0.6; 0.65)$$. Using deep-field imaging data, we extrapolate galaxy populations beyond the limiting magnitude of DES Y3 and calculate the properties of cluster member galaxies via statistical background subtraction. We construct mock galaxy clusters as random draws from a distribution function, and render mock clusters and line-of-sight catalogues into synthetic images in the same format as actual survey observations. Synthetic galaxy clusters are generated from real observational data, and thus are independent from the assumptions inherent to cosmological simulations. The recipe can be straightforwardly modified to incorporate extra information, and correct for survey incompleteness. New realizations of synthetic clusters can be created at minimal cost, which will allow future analyses to generate the large number of images needed to characterize systematic uncertainties in cluster mass measurements. 

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 mean-shifting method of marginalizing over redshift uncertainty, validating that this simpler model is sufficient for use in the DES Year 3 cosmology results presented in companion papers.