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1. The STRong lensing Insights into the Dark Energy Survey (STRIDES) 2017/2018 follow-up campaign: discovery of 10 lensed quasars and 10 quasar pairs

   https://doi.org/10.1093/mnras/staa652  

   Lemon, C ; Auger, M W ; McMahon, R ; Anguita, T ; Apostolovski, Y ; Chen, G C-F ; Fassnacht, C D ; Melo, A D ; Motta, V ; Shajib, A ; et al ( May 2020 , Monthly Notices of the Royal Astronomical Society)

   ABSTRACT We report the results of the STRong lensing Insights into the Dark Energy Survey (STRIDES) follow-up campaign of the late 2017/early 2018 season. We obtained spectra of 65 lensed quasar candidates with ESO Faint Object Spectrograph and Camera 2 on the NTT and Echellette Spectrograph and Imager on Keck, confirming 10 new lensed quasars and 10 quasar pairs. Eight lensed quasars are doubly imaged with source redshifts between 0.99 and 2.90, one is triply imaged (DESJ0345−2545, z = 1.68), and one is quadruply imaged (quad: DESJ0053−2012, z = 3.8). Singular isothermal ellipsoid models for the doubles, based on high-resolution imaging from SAMI on Southern Astrophysical Research Telescope or Near InfraRed Camera 2 on Keck, give total magnifications between 3.2 and 5.6, and Einstein radii between 0.49 and 1.97 arcsec. After spectroscopic follow-up, we extract multi-epoch grizY photometry of confirmed lensed quasars and contaminant quasar + star pairs from DES data using parametric multiband modelling, and compare variability in each system’s components. By measuring the reduced χ2 associated with fitting all epochs to the same magnitude, we find a simple cut on the less variable component that retains all confirmed lensed quasars, while removing 94 per cent of contaminant systems. Based on our spectroscopic follow-up, thismore »variability information improves selection of lensed quasars and quasar pairs from 34-45 per cent to 51–70 per cent, with most remaining contaminants being star-forming galaxies. Using mock lensed quasar light curves we demonstrate that selection based only on variability will over-represent the quad fraction by 10 per cent over a complete DES magnitude-limited sample, explained by the magnification bias and hence lower luminosity/more variable sources in quads.« less
2. Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks

   https://doi.org/10.1093/mnras/stac3228  

   Cheng, T-Y ; Domínguez Sánchez, H. ; Vega-Ferrero, J. ; Conselice, C. J. ; Siudek, M. ; Aragón-Salamanca, A. ; Bernardi, M. ; Cooke, R. ; Ferreira, L. ; Huertas-Company, M. ; et al ( November 2022 , Monthly Notices of the Royal Astronomical Society)

   We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of ∼21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN – monochromatic images versus gri-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES (i < 18), while the other is trained with bright galaxies (r < 17.5) and ‘emulated’ galaxies up to r-band magnitude 22.5. Despite the different approaches, the agreement between the two catalogues is excellent up to i < 19, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on gri-band images, at least in the bright regime. At fainter magnitudes, i > 19, the overall agreement is good (∼95 per cent), but is mostly driven by the large spiral fraction in the two catalogues. In contrast,more »the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases, we are able to identify lenticular galaxies (at least up to i < 19), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies.

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3. Search for globular clusters associated with the Milky Way dwarf galaxies using Gaia DR2

   https://doi.org/10.1093/mnras/staa3297  

   Huang, Kuan-Wei ; Koposov, Sergey E ( November 2020 , Monthly Notices of the Royal Astronomical Society)

   ABSTRACT We report the result of searching for globular clusters (GCs) around 55 Milky Way (MW) satellite dwarf galaxies within the distance of 450 kpc from the Galactic Centre except for the Large and Small Magellanic Clouds and the Sagittarius dwarf. For each dwarf, we analyse the stellar distribution of sources in Gaia DR2, selected by magnitude, proper motion, and source morphology. Using the kernel density estimation of stellar number counts, we identify 11 possible GC candidates. Cross-matched with existing imaging data, all 11 objects are known either GCs or galaxies and only Fornax GC 1–6 among them are associated with the targeted dwarf galaxy. Using simulated GCs, we calculate the GC detection limit $M_{\rm V}^{\rm lim}$ that spans the range from $M_{\rm V}^{\rm lim}\sim -7$ for distant dwarfs to $M_{\rm V}^{\rm lim}\sim 0$ for nearby systems. Assuming a Gaussian GC luminosity function, we compute that the completeness of the GC search is above 90 per cent for most dwarf galaxies. We construct the 90 per cent credible intervals/upper limits on the GC specific frequency SN of the MW dwarf galaxies: 12 < SN < 47 for Fornax, SN < 20 for the dwarfs with −12 < MV < −10, SNmore »< 30 for the dwarfs with −10 < MV < −7, and SN < 90 for the dwarfs with MV > −7. Based on SN, we derive the probability of galaxies hosting GCs given their luminosity, finding that the probability of galaxies fainter than MV = −9 to host GCs is lower than 0.1.« less
4. Deblending and classifying astronomical sources with Mask R-CNN deep learning

   https://doi.org/10.1093/mnras/stz2845  

   Burke, Colin J. ; Aleo, Patrick D. ; Chen, Yu-Ching ; Liu, Xin ; Peterson, John R. ; Sembroski, Glenn H. ; Lin, Joshua Yao-Yu ( October 2019 , Monthly Notices of the Royal Astronomical Society)

   We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.
5. Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning

   https://doi.org/10.3847/1538-4357/ace4ba  

   Zaborowski, E. A. ; Drlica-Wagner, A. ; Ashmead, F. ; Wu, J. F. ; Morgan, R. ; Bom, C. R. ; Shajib, A. J. ; Birrer, S. ; Cerny, W. ; Buckley-Geer, E. J. ; et al ( August 2023 , The Astrophysical Journal)

   We perform a search for galaxy–galaxy strong lens systems using a convolutional neural network (CNN) applied to imaging data from the first public data release of the DECam Local Volume Exploration Survey, which contains ∼520 million astronomical sources covering ∼4000 deg²of the southern sky to a 5σpoint–source depth ofg= 24.3,r= 23.9,i= 23.3, andz= 22.8 mag. Following the methodology of similar searches using Dark Energy Camera data, we apply color and magnitude cuts to select a catalog of ∼11 million extended astronomical sources. After scoring with our CNN, the highest-scoring 50,000 images were visually inspected and assigned a score on a scale from 0 (not a lens) to 3 (very probable lens). We present a list of 581 strong lens candidates, 562 of which are previously unreported. We categorize our candidates using their human-assigned scores, resulting in 55 Grade A candidates, 149 Grade B candidates, and 377 Grade C candidates. We additionally highlight eight potential quadruply lensed quasars from this sample. Due to the location of our search footprint in the northern Galactic cap (b> 10 deg) and southern celestial hemisphere (decl. < 0 deg), our candidate list has little overlap with other existing ground-based searches. Where our search footprintmore »does overlap with other searches, we find a significant number of high-quality candidates that were previously unidentified, indicating a degree of orthogonality in our methodology. We report properties of our candidates including apparent magnitude and Einstein radius estimated from the image separation.

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