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We present a sample of 706, z < 1.5 active galactic nuclei (AGNs) selected from optical photometric variability in three of the Dark Energy Survey (DES) deep fields (E2, C3, and X3) over an area of 4.64 deg2. We construct light curves using difference imaging aperture photometry for resolved sources and non-difference imaging PSF photometry for unresolved sources, respectively, and characterize the variability significance. Our DES light curves have a mean cadence of 7 d, a 6-yr baseline, and a single-epoch imaging depth of up to g ∼ 24.5. Using spectral energy distribution (SED) fitting, we find 26 out of total 706 variable galaxies are consistent with dwarf galaxies with a reliable stellar mass estimate (⁠|$M_{\ast }\lt 10^{9.5}\, {\rm M}_\odot$|⁠ ; median photometric redshift of 0.9). We were able to constrain rapid characteristic variability time-scales (∼ weeks) using the DES light curves in 15 dwarf AGN candidates (a subset of our variable AGN candidates) at a median photometric redshift of 0.4. This rapid variability is consistent with their low black hole (BH) masses. We confirm the low-mass AGN nature of one source with a high S/N optical spectrum. We publish our catalogue, optical light curves, and supplementary data, such as X-ray properties and optical spectra, when available. We measure a variable AGN fraction versus stellar mass and compare to results from a forward model. This work demonstrates the feasibility of optical variability to identify AGNs with lower BH masses in deep fields, which may be more 'pristine' analogues of supermassive BH seeds. [ABSTRACT FROM AUTHOR]

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies' labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result). [ABSTRACT FROM AUTHOR]

We present a large-scale galaxy structure Cl J021734-0513 at z~ 0.65 discovered in the UKIDSS UDS field, made of~20 galaxy groups and clusters, spreading over 10Mpc. We report on a VLT/VIMOS spectroscopic follow-up program that, combined with past spectroscopy, allowed us to confirm four galaxy clusters (M200~ 1014 M~) and a dozen associated groups and star-forming galaxy overdensities. Two additional filamentary structures at z~0.62 and 0.69 and foreground and background clusters at 0.6 < z < 0.7 were also confirmed along the line of sight. The structure subcomponents are at different formation stages. The clusters have a core dominated by passive galaxies and an established red sequence. The remaining structures are a mix of star-forming galaxy overdensities and forming groups. The presence of quiescent galaxies in the core of the latter shows that 'pre-processing' has already happened before the groups fall into theirmoremassive neighbours. Our spectroscopy allows us to derive spectral index measurements e.g. emission/absorption line equivalent widths, strength of the 4000Å break, valuable to investigate the star formation history of structure members. Based on these line measurements, we select a population of 'post-starburst' galaxies. These galaxies are preferentially found within the virial radius of clusters, supporting a scenario in which their recent quenching could be prompted by gas stripping by the dense intracluster medium. We derive stellar age estimates using Markov Chain Monte Carlo-based spectral fitting for quiescent galaxies and find a correlation between ages and colours/stellar masses which favours a top-down formation scenario of the red sequence. A catalogue of~650 redshifts in UDS is released alongside the paper (via MNRAS online data). [ABSTRACT FROM AUTHOR]

We present a large-scale galaxy structure Cl J021734-0513 at z ∼ 0.65 discovered in the UKIDSS UDS field, made of ∼20 galaxy groups and clusters, spreading over 10Mpc. We report on a VLT/VIMOS spectroscopic follow-up program that, combined with past spectroscopy, allowed us to confirm four galaxy clusters (M200 ∼ 1014 M☉) and a dozen associated groups and star-forming galaxy overdensities. Two additional filamentary structures at z∼0.62 and 0.69 and foreground and background clusters at 0.6 < ʑ < 0.7 were also confirmed along the line of sight. The structure subcomponents are at different formation stages. The clusters have a core dominated by passive galaxies and an established red sequence. The remaining structures are a mix of star-forming galaxy overdensities and forming groups. The presence of quiescent galaxies in the core of the latter shows that 'pre-processing' has already happened before the groups fall into theirmoremassive neighbours. Our spectroscopy allows us to derive spectral index measurements e.g. emission/absorption line equivalent widths, strength of the 4000Å break, valuable to investigate the star formation history of structure members. Based on these line measurements, we select a population of 'post-starburst' galaxies. These galaxies are preferentially found within the virial radius of clusters, supporting a scenario in which their recent quenching could be prompted by gas stripping by the dense intracluster medium. We derive stellar age estimates using Markov Chain Monte Carlo-based spectral fitting for quiescent galaxies and find a correlation between ages and colours/stellar masses which favours a top-down formation scenario of the red sequence. A catalogue of ∼650 redshifts in UDS is released alongside the paper (via MNRAS online data). [ABSTRACT FROM AUTHOR]