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1. Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning

   https://doi.org/10.1093/mnras/stac233  

   Tarsitano, F. ; Bruderer, C. ; Schawinski, K. ; Hartley, W. G. ( February 2022 , Monthly Notices of the Royal Astronomical Society)

   In this work, we explore the possibility of applying machine learning methods designed for 1D problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly steps, such as the point spread function deconvolution and the training and application of complex Convolutional Neural Networks of thousands or even millions of parameters. In our approach, we extract features from the galaxy images by analysing the elliptical isophotes in their light distribution and collect the information in a sequence. The sequences obtained with this method present definite features allowing a direct distinction between galaxy types. Then, we train and classify the sequences with machine learning algorithms, designed through the platform Modulos AutoML. As a demonstration of this method, we use the second public release of the Dark Energy Survey (DES DR2). We show that we are able to successfully distinguish between early-type and late-type galaxies, for images with signal-to-noise ratio greater than 300. This yields an accuracy of $86{{\ \rm per\ cent}}$ for the early-type galaxies and $93{{\ \rm per\ cent}}$ for the late-type galaxies, which is on par with most contemporary automated image classification approaches. The data dimensionality reduction of our novelmore »method implies a significant lowering in computational cost of classification. In the perspective of future data sets obtained with e.g. Euclid and the Vera Rubin Observatory, this work represents a path towards using a well-tested and widely used platform from industry in efficiently tackling galaxy classification problems at the peta-byte scale.

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2. Dwarf AGNs from Optical Variability for the Origins of Seeds (DAVOS): insights from the dark energy survey deep fields

   https://doi.org/10.1093/mnras/stac2262  

   Burke, Colin J ; Liu, Xin ; Shen, Yue ; Phadke, Kedar A ; Yang, Qian ; Hartley, Will G ; Harrison, Ian ; Palmese, Antonella ; Guo, Hengxiao ; Zhang, Kaiwen ; et al ( September 2022 , Monthly Notices of the Royal Astronomical Society)

   ABSTRACT 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 propertiesmore »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.« less
3. Pushing automated morphological classifications to their limits with the Dark Energy Survey

   https://doi.org/10.1093/mnras/stab594  

   Vega-Ferrero, J ; Sánchez, H Domínguez ; Bernardi, M ; Huertas-Company, M ; Morgan, R ; Margalef, B ; Aguena, M ; Allam, S ; Annis, J ; Avila, S ; et al ( June 2021 , Monthly Notices of the Royal Astronomical Society)

   Abstract We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs), and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97% accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼ 87% and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample andmore »to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ε) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.« less
4. The southern stellar stream spectroscopic survey (S5): Overview, target selection, data reduction, validation, and early science

   https://doi.org/10.1093/mnras/stz2731  

   Li, T. S. ; Koposov, S. E. ; Zucker, D. B. ; Lewis, G. F. ; Kuehn, K. ; Simpson, J. D. ; Ji, A. P. ; Shipp, N. ; Mao, Y-Y ; Geha, M. ; et al ( October 2019 , Monthly Notices of the Royal Astronomical Society)

   We introduce the southern stellar stream spectroscopy survey (S5), an on-going program to map the kinematics and chemistry of stellar streams in the southern hemisphere. The initial focus of S5 has been spectroscopic observations of recently identified streams within the footprint of the dark energy survey (DES), with the eventual goal of surveying streams across the entire southern sky. Stellar streams are composed of material that has been tidally striped from dwarf galaxies and globular clusters and hence are excellent dynamical probes of the gravitational potential of the Milky Way, as well as providing a detailed snapshot of its accretion history. Observing with the 3.9 m Anglo-Australian Telescope’s 2-degree-Field fibre positioner and AAOmega spectrograph, and combining the precise photometry of DES DR1 with the superb proper motions from Gaia DR2, allows us to conduct an efficient spectroscopic survey to map these stellar streams. So far S5 has mapped nine DES streams and three streams outside of DES; the former are the first spectroscopic observations of these recently discovered streams. In addition to the stream survey, we use spare fibres to undertake a Milky Way halo survey and a low-redshift galaxy survey. This paper presents an overview of the S5 program,more »describing the scientific motivation for the survey, target selection, observation strategy, data reduction, and survey validation. Finally, we describe early science results on stellar streams and Milky Way halo stars drawn from the survey. Updates on S5, including future public data releases, can be found at http://s5collab.github.io.

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5. The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space

   https://doi.org/10.1093/mnras/stad2013  

   Samuroff, S. ; Mandelbaum, R. ; Blazek, J. ; Campos, A. ; MacCrann, N. ; Zacharegkas, G. ; Amon, A. ; Prat, J. ; Singh, S. ; Elvin-Poole, J. ; et al ( July 2023 , Monthly Notices of the Royal Astronomical Society)

   We present direct constraints on galaxy intrinsic alignments (IAs) using the Dark Energy Survey Year 3 (DES Y3), the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and its precursor, the Baryon Oscillation Spectroscopic Survey (BOSS). Our measurements incorporate photometric red sequence (redMaGiC) galaxies from DES with median redshift z ∼ 0.2–1.0, luminous red galaxies from eBOSS at z ∼ 0.8, and also an SDSS-III BOSS CMASS sample at z ∼ 0.5. We measure two-point IA correlations, which we fit using a model that includes lensing, magnification, and photometric redshift error. Fitting on scales 6 Mpc h−1 < rp < 70 Mpc h−1, we make a detection of IAs in each sample, at 5σ–22σ (assuming a simple one-parameter model for IAs). Using these red samples, we measure the IA–luminosity relation. Our results are statistically consistent with previous results, but offer a significant improvement in constraining power, particularly at low luminosity. With this improved precision, we see detectable dependence on colour between broadly defined red samples. It is likely that a more sophisticated approach than a binary red/blue split, which jointly considers colour and luminosity dependence in the IA signal, will be needed in future. We also compare the various signal components at themore »best-fitting point in parameter space for each sample, and find that magnification and lensing contribute $\sim 2\!-\!18~{{\ \rm per\ cent}}$ of the total signal. As precision continues to improve, it will certainly be necessary to account for these effects in future direct IA measurements. Finally, we make equivalent measurements on a sample of emission-line galaxies from eBOSS at z ∼ 0.8. We constrain the non-linear alignment amplitude to be $A_1=0.07^{+0.32}_{-0.42}$ (|A1| < 0.78 at 95 per cent CL).

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