We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g − i < 5, 0.6 < g − r < 3, r mag > 19, g mag > 20, and i mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as 'probably' or 'definitely' lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

We present time-delay measurements for the new quadruple imaged quasar DES J04085354, the first quadruple imaged quasar found in the Dark Energy Survey (DES). Our result is made possible by implementing a new observational strategy using almost daily observations with the MPIA 2.2 m telescope at La Silla observatory and deep exposures reaching a signal-to-noise ratio of about 1000 per quasar image. This data qualityallows us to catch small photometric variations (a few mmag rms) of the quasar, acting on temporal scales much shorter than microlensing, and hence making the time delay measurement very robust against microlensing. In only seven months we very accurately measured one of the time delays in DES J04085354: - "t(AB) = 112.1 ± 2.1 days (1.8%) using only the MPIA 2.2 m data. In combination with data taken with the 1.2 m Euler Swiss telescope, we also measured two delays involving the D component of the system - "t(AD) = ± 12.8 days (8.2%) and - "t(BD) = 42.4 ± 17.6 days (41%), where all the error bars include systematics. Turning these time delays into cosmological constraints will require deep Hubble Space Telescope (HST) imaging or ground-based adaptive optics (AO), and information on the velocity field of the lensing galaxy.

The correlation between the broad line region radius and continuum luminosity (R-L relation) of active galactic nuclei (AGNs) is critical for single-epoch mass estimates of supermassive black holes (SMBHs). At z ∼ 1-2, where AGN activity peaks, the R-L relation is constrained by the reverberation mapping (RM) lags of the Mg II line. We present 25 Mg II lags from the Australian Dark Energy Survey RM project based on 6 yr of monitoring. We define quantitative criteria to select good lag measurements and verify their reliability with simulations based on both the damped random walk stochastic model and the rescaled, resampled versions of the observed light curves of local, well-measured AGN. Our sample significantly increases the number of Mg II lags and extends the R-L relation to higher redshifts and luminosities. The relative iron line strength RFe has little impact on the R-L relation. The best-fitting Mg II R-L relation has a slope α = 0.39 ± 0.08 with an intrinsic scatter σrl = 0.15+−000203. The slope is consistent with previous measurements and shallower than the H β R-L relation. The intrinsic scatter of the new R-L relation is substantially smaller than previous studies and comparable to the intrinsic scatter of the H β R-L relation. Our new R-L relation will enable more precise single-epoch mass estimates and SMBH demographic studies at cosmic noon.

Calibration uncertainties have been the leading systematic uncertainty in recent analyses using Type Ia supernovae (SNe Ia) to measure cosmological parameters. To improve the calibration, we present the application of spectral energy distribution-dependent 'chromatic corrections' to the SN light-curve photometry from the Dark Energy Survey (DES). These corrections depend on the combined atmospheric and instrumental transmission function for each exposure, and they affect photometry at the 0.01 mag (1 per cent) level, comparable to systematic uncertainties in calibration and photometry. Fitting our combined DES and low-z SN Ia sample with baryon acoustic oscillation (BAO) and cosmic microwave background (CMB) priors for the cosmological parameters m (the fraction of the critical density of the universe comprised of matter) and w (the dark energy equation of state parameter), we compare those parameters before and after applying the corrections. We find the change in w and m due to not including chromatic corrections is −0.002 and 0.000, respectively, for the DES-SN3YR sample with BAO and CMB priors, consistent with a larger DES-SN3YR-like simulation, which has a w-change of 0.0005 with an uncertainty of 0.008 and an m change of 0.000 with an uncertainty of 0.002. However, when considering samples on individual CCDs we find large redshift-dependent biases (∼0.02 in distance modulus) for SN distances.

We report the results of searches for strong gravitational lens systems in the Dark Energy Survey (DES) Science Verification and Year 1 observations. The Science Verification data span approximately 250 sq. deg. with a median i-band limiting magnitude for extended objects (10σ) of 23.0. The Year 1 data span approximately 2000 sq. deg. and have an i-band limiting magnitude for extended objects (10σ) of 22.9. As these data sets are both wide and deep, they are particularly useful for identifying strong gravitational lens candidates. Potential strong gravitational lens candidate systems were initially identified based on a color and magnitude selection in the DES object catalogs or because the system is at the location of a previously identified galaxy cluster. Cutout images of potential candidates were then visually scanned using an object viewer and numerically ranked according to whether or not we judged them to be likely strong gravitational lens systems. Having scanned nearly 400,000 cutouts, we present 374 candidate strong lens systems, of which 348 are identified for the first time. We provide the R.A. and decl., the magnitudes and photometric properties of the lens and source objects, and the distance (radius) of the source(s) from the lens center for each system.

We report the discovery of two new candidate stellar systems in the constellation of Cetus using the data from the first two years of the Dark Energy Survey (DES). The objects, DES J0111-1341 and DES J0225+0304, are located at a heliocentric distance of ~25 kpc and appear to have old and metal-poor populations. Their distances to the Sagittarius orbital plane, ~1.73 kpc (DES J0111-1341) and ~0.50 kpc (DES J0225+0304), indicate that they are possibly associated with the Sagittarius dwarf stream. The half-light radius (rh ≃ 4.55 pc) and luminosity (MV ≃ +0.3) of DES J0111-1341 are consistent with it being an ultrafaint stellar cluster, while the half-light radius (rh ≃ 18.55 pc) and luminosity (MV ≃ -1.1) of DES J0225+0304 place it in an ambiguous region of size-luminosity space between stellar clusters and dwarf galaxies. Determinations of the characteristic parameters of the Sagittarius stream, metallicity spread (-2.18 ≲ [Fe/H] ≲ -0.95) and distance gradient (23 kpc ≲ D⊙ ≲ 29 kpc), within the DES footprint in the Southern hemisphere, using the same DES data, also indicate a possible association between these systems. If these objects are confirmed through spectroscopic follow-up to be gravitationally bound systems and to share a Galactic trajectory with the Sagittarius stream, DES J0111-1341 and DES J0225+0304 would be the first ultrafaint stellar systems associated with the Sagittarius stream. Furthermore, DES J0225+0304 would also be the first confirmed case of an ultrafaint satellite of a satellite.

We present spectroscopic confirmation of two new lensed quasars via data obtained at the 6.5m Magellan/Baade Telescope. The lens candidates have been selected from the Dark Energy Survey (DES) and WISE based on their multi-band photometry and extended morphology in DES images. Images of DES J0115-5244 show two blue point sources at either side of a red galaxy. Our long-slit data confirm that both point sources are images of the same quasar at $z_{s}=1.64.$ The Einstein Radius estimated from the DES images is $0.51$". DES J2200+0110 is in the area of overlap between DES and the Sloan Digital Sky Survey (SDSS). Two blue components are visible in the DES and SDSS images. The SDSS fiber spectrum shows a quasar component at $z_{s}=2.38$ and absorption compatible with Mg II and Fe II at $z_{l}=0.799$, which we tentatively associate with the foreground lens galaxy. The long-slit Magellan spectra show that the blue components are resolved images of the same quasar. The Einstein Radius is $0.68$" corresponding to an enclosed mass of $1.6\times10^{11}\,M_{\odot}.$ Three other candidates were observed and rejected, two being low-redshift pairs of starburst galaxies, and one being a quasar behind a blue star. These first confirmation results provide an important empirical validation of the data-mining and model-based selection that is being applied to the entire DES dataset.

The joint analysis of galaxy-galaxy lensing and galaxy clustering is a promising method for inferring the growth function of large-scale structure. Anticipating a near future application of this analysis to Dark Energy Survey (DES) measurements of galaxy positions and shapes, we develop a practical approach to modeling the assumptions and systematic effects affecting the joint analysis of small-scale galaxy-galaxy lensing and large-scale galaxy clustering. Introducing parameters that characterize the halo occupation distribution (HOD), photometric redshift uncertainties, and shear measurement errors, we study how external priors on different subsets of these parameters affect our growth constraints. Degeneracies within the HOD model, as well as between the HOD and the growth function, are identified as the dominant source of complication, with other systematic effects being subdominant. The impact of HOD parameters and their degeneracies necessitate the detailed joint modeling of the galaxy sample that we employ. We conclude that DES data will provide powerful constraints on the evolution of structure growth in the Universe, conservatively/optimistically constraining the growth function to 7.9%/4.8% with its first-year data that cover over 1000 square degrees, and to 3.9%/2.3% with its full five-year data that will survey 5000 square degrees, including both statistical and systematic uncertainties.

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (2013 September through 2014 February) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0% of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm's performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at http://portal.nserc.gov/project/dessn/autoscan.