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Discovery of a Candidate Binary Supermassive Black Hole in a Periodic Quasar from Circumbinary Accretion Variability, , , , , , , , , и 61 other автор(ы). (2020)cite arxiv:2008.12317Comment: resubmitted to MNRAS after addressing referee reports; comments are welcome.Constraints on Decaying Dark Matter with DES-Y1 and external data, , , , , , , , , и 75 other автор(ы). (2020)cite arxiv:2011.04606Comment: 23 pages, 11 figures. DES extensions group.VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning, , , , , , , , , и 49 other автор(ы). (2016)cite arxiv:1607.01391Comment: 11 pages, 7 figures, 7 tables, MNRAS submitted.Dark Energy Survey Year 1 Results: Constraining Baryonic Physics in the Universe, , , , , , , , , и 67 other автор(ы). (2020)cite arxiv:2007.15026Comment: 24 pages, 18 figures, 2 tables. submitted to MNRAS. A brief video summary of this paper is available at https://www.youtube.com/watch?v=QbeNwk5papU.Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects., , , , , , , , , и 49 other автор(ы). CoRR, (2020)Redshift Measurement and Spectral Classification for eBOSS Galaxies with the Redmonster Software, , , , , , , , , и 6 other автор(ы). (2016)cite arxiv:1607.02432Comment: 19 pages, 16 figures.A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest., , , , , , , , , и 63 other автор(ы). CoRR, (2020)Extreme variability quasars from the Sloan Digital Sky Survey and the Dark Energy Survey, , , , , , , , , и 42 other автор(ы). (2017)cite arxiv:1706.07875Comment: submitted to ApJ; comments welcome.Crowdsourcing quality control for Dark Energy Survey images., , , , , , , , , и 40 other автор(ы). Astron. Comput., (2016)DES science portal: Creating science-ready catalogs., , , , , , , , , и 48 other автор(ы). Astron. Comput., (2018)