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1. Weak Gravitational Lensing around Low Surface Brightness Galaxies in the DES Year 3 Data

   https://doi.org/10.33232/001c.124536  

   Chicoine, N; Prat, J; Zacharegkas, G; Chang, C; Tanoglidis, D; Drlica-Wagner, A; Anbajagane, D; Adhikari, S; Amon, A; Wechsler, R H; et al (January 2024, The Open Journal of Astrophysics)

   We present galaxy-galaxy lensing measurements using a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data as lenses. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. These dark-matter-dominated objects are intriguing due to potentially unusual formation channels that lead to their diffuse stellar component. Given the faintness of LSBGs, using standard observational techniques to characterize their total masses proves challenging. Weak gravitational lensing, which is less sensitive to the stellar component of galaxies, could be a promising avenue to estimate the masses of LSBGs. Our LSBG sample consists of 23,790 galaxies separated into red and blue color types at $g-i\ge 0.60$and $g-i<0.60$, respectively. Combined with the DES Y3 shear catalog, we measure the tangential shear around these LSBGs and find signal-to-noise ratios of 6.67 for the red sample, 2.17 for the blue sample, and 5.30 for the full sample. We use the clustering redshifts method to obtain redshift distributions for the red and blue LSBG samples. Assuming all red LSBGs are satellites, we fit a simple model to the measurements and estimate the host halo mass of these LSBGs to be . We place a 95% upper bound on the subhalo mass at . By contrast, we assume the blue LSBGs are centrals, and place a 95% upper bound on the halo mass at $log\left(M_{\mathrm{h}\mathrm{o}\mathrm{s}\mathrm{t}}/M_{\odot }\right)<11.84$. We find that the stellar-to-halo mass ratio of the LSBG samples is consistent with that of the general galaxy population. This work illustrates the viability of using weak gravitational lensing to constrain the halo masses of LSBGs. 

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2. Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images

   Tanoglidis, Dimitrios; Ciprijanovic, Aleksandra; Drlica-Wagner, Alex; Nord, Brian; Wang, Michael H.; Amsellem, Ariel J.; Downey, Kathryn; Jenkins, Sydney; Kafkes, Diana; Zhang, Zhuoqi (January 2021, Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS))

   Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as “ghosting artifacts” or “ghosts”) and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scatteredlight artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that lead to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys. 

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3. Environmental Quenching of Low-surface-brightness Galaxies Near Hosts from Large Magellanic Cloud to Milky Way Mass Scales

   https://doi.org/10.3847/1538-4357/ad79fe  

   Bhattacharyya, J.; Peter, A_H_G; Martini, P.; Mutlu-Pakdil, B.; Drlica-Wagner, A.; Pace, A_B; Strigari, L_E; Cheng, T-Y; Roberts, D.; Tanoglidis, D.; et al (November 2024, The Astrophysical Journal)

   Abstract Low-surface-brightness galaxies (LSBGs) are excellent probes of quenching and other environmental processes near massive galaxies. We study an extensive sample of LSBGs near massive hosts in the local universe that are distributed across a diverse range of environments. The LSBGs with surface-brightness ${\mu }_{\mathrm{eff},\mathit{g}}>24.2\mathrm{mag}{\mathrm{arcsec}}^{-2}$are drawn from the Dark Energy Survey Year 3 catalog while the hosts with masses $9.0<\log \left({\mathcal{M}}_{\star }/M_{\odot }\right)<11.0$comparable to the Milky Way and the Large Magellanic Cloud are selected from the z0MGS sample. We study the projected radial density profiles of LSBGs as a function of their color and surface brightness around hosts in both the rich Fornax–Eridanus cluster environment and the low-density field. We detect an overdensity with respect to the background density, out to 2.5 times the virial radius for both hosts in the cluster environment and the isolated field galaxies. When the LSBG sample is split byg−icolor or surface brightnessμ_eff,g, we find the LSBGs closer to their hosts are significantly redder and brighter, like their high-surface-brightness counterparts. The LSBGs form a clear “red sequence” in both the cluster and isolated environments that is visible beyond the virial radius of the hosts. This suggests preprocessing of infalling LSBGs and a quenched backsplash population around both host samples. More so, the relative prominence of the “blue cloud” feature implies that preprocessing is ongoing near the isolated hosts compared to the cluster environment where the LSBGs are already well processed. 

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4. 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)

   null (Ed.)

   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 and 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. 

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5. A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2

   https://doi.org/10.3847/1538-4365/ac9ea8  

   Yang, Qian; Shen, Yue (December 2022, The Astrophysical Journal Supplement Series)

   Abstract We present a catalog of 1.4 million photometrically selected quasar candidates in the southern hemisphere over the ∼5000 deg²Dark Energy Survey (DES) wide survey area. We combine optical photometry from the DES second data release (DR2) with available near-infrared (NIR) and the all-sky unWISE mid-infrared photometry in the selection. We build models of quasars, galaxies, and stars with multivariate skew-tdistributions in the multidimensional space of relative fluxes as functions of redshift (or color for stars) and magnitude. Our selection algorithm assigns probabilities for quasars, galaxies, and stars and simultaneously calculates photometric redshifts (photo-z) for quasar and galaxy candidates. Benchmarking on spectroscopically confirmed objects, we successfully classify (with photometry) 94.7% of quasars, 99.3% of galaxies, and 96.3% of stars when all IR bands (NIRYJHKand WISE W1W2) are available. The classification and photo-zregression success rates decrease when fewer bands are available. Our quasar (galaxy) photo-zquality, defined as the fraction of objects with the difference between the photo-z z_pand the spectroscopic redshiftz_s, ∣Δz∣ ≡ ∣z_s−z_p∣/(1 +z_s) ≤ 0.1, is 92.2% (98.1%) when all IR bands are available, decreasing to 72.2% (90.0%) using optical DES data only. Our photometric quasar catalog achieves an estimated completeness of 89% and purity of 79% atr< 21.5 (0.68 million quasar candidates), with reduced completeness and purity at 21.5 <r≲ 24. Among the 1.4 million quasar candidates, 87,857 have existing spectra, and 84,978 (96.7%) of them are spectroscopically confirmed quasars. Finally, we provide quasar, galaxy, and star probabilities for all (0.69 billion) photometric sources in the DES DR2 coadded photometric catalog. 

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