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ABSTRACT We present multiwavelength spectral and temporal variability analysis of PKS 0027-426 using optical griz observations from Dark Energy Survey between 2013 and 2018 and VEILS Optical Light curves of Extragalactic TransienT Events (VOILETTE) between 2018 and 2019 and near-infrared (NIR) JKs observations from Visible and Infrared Survey Telescope for Astronomy Extragalactic Infrared Legacy Survey (VEILS) between 2017 and 2019. Multiple methods of cross-correlation of each combination of light curve provides measurements of possible lags between optical–optical, optical–NIR, and NIR–NIR emission, for each observation season and for the entire observational period. Inter-band time lag measurements consistently suggest either simultaneous emission or delays between emission regions on time-scales smaller than the cadences of observations. The colour–magnitude relation between each combination of filters was also studied to determine the spectral behaviour of PKS 0027-426. Our results demonstrate complex colour behaviour that changes between bluer when brighter, stable when brighter, and redder when brighter trends over different time-scales and using different combinations of optical filters. Additional analysis of the optical spectra is performed to provide further understanding of this complex spectral behaviour. 

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 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 Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample. 

ABSTRACT Rapidly evolving transients (RETs), also termed fast blue optical transients, are a recently discovered group of astrophysical events that display rapid luminosity evolution. RETs typically rise to peak in less than 10 d and fade within 30, a time-scale unlikely to be compatible with the decay of Nickel-56 that drives conventional supernovae (SNe). Their peak luminosity spans a range of −15 < Mg < −22.5, with some events observed at redshifts greater than 1. Their evolution on fast time-scales has hindered high-quality follow-up observations, and thus their origin and explosion/emission mechanism remains unexplained. In this paper, we present the largest sample of RETs to date, comprising 106 objects discovered by the Dark Energy Survey, and perform the most comprehensive analysis of RET host galaxies. Using deep-stacked photometry and emission lines from OzDES spectroscopy, we derive stellar masses and star formation rates (SFRs) for 49 host galaxies, and metallicities ([O/H]) for 37. We find that RETs explode exclusively in star-forming galaxies and are thus likely associated with massive stars. Comparing RET hosts to samples of host galaxies of other explosive transients as well as field galaxies, we find that RETs prefer galaxies with high specific SFRs (〈log (sSFR)〉 ∼ −9.6), indicating a link to young stellar populations, similar to stripped-envelope SNe. RET hosts appear to show a lack of chemical enrichment, their metallicities akin to long-duration gamma-ray bursts and superluminous SN host galaxies (〈12 + log (O/H)〉 ∼ 9.4). There are no clear relationships between mass or SFR of the host galaxies and the peak magnitudes or decline rates of the transients themselves. 

ABSTRACT We consider the effects of weak gravitational lensing on observations of 196 spectroscopically confirmed Type Ia Supernovae (SNe Ia) from years 1 to 3 of the Dark Energy Survey (DES). We simultaneously measure both the angular correlation function and the non-Gaussian skewness caused by weak lensing. This approach has the advantage of being insensitive to the intrinsic dispersion of SNe Ia magnitudes. We model the amplitude of both effects as a function of σ8, and find σ8 =1.2$$^{+0.9}_{-0.8}$$. We also apply our method to a subsample of 488 SNe from the Joint Light-curve Analysis (JLA; chosen to match the redshift range we use for this work), and find σ8 =0.8$$^{+1.1}_{-0.7}$$. The comparable uncertainty in σ8 between DES–SN and the larger number of SNe from JLA highlights the benefits of homogeneity of the DES–SN sample, and improvements in the calibration and data analysis.