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Constraints on Decaying Dark Matter with DES-Y1 and external data, , , , , , , , , and 75 other author(s). (2020)cite arxiv:2011.04606Comment: 23 pages, 11 figures. DES extensions group.Dark Energy Survey Year 1 Results: Constraining Baryonic Physics in the Universe, , , , , , , , , and 67 other author(s). (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.Discovery of a Candidate Binary Supermassive Black Hole in a Periodic Quasar from Circumbinary Accretion Variability, , , , , , , , , and 61 other author(s). (2020)cite arxiv:2008.12317Comment: resubmitted to MNRAS after addressing referee reports; comments are welcome.Extreme variability quasars from the Sloan Digital Sky Survey and the Dark Energy Survey, , , , , , , , , and 42 other author(s). (2017)cite arxiv:1706.07875Comment: submitted to ApJ; comments welcome.VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning, , , , , , , , , and 49 other author(s). (2016)cite arxiv:1607.01391Comment: 11 pages, 7 figures, 7 tables, MNRAS submitted.The SDSS-III BOSS quasar lens survey: discovery of thirteen gravitationally lensed quasars, , , , , , , , , and 8 other author(s). (2015)cite arxiv:1509.07917Comment: 12 pages, 3 figures, 6 tables, MNRAS submitted.Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects., , , , , , , , , and 49 other author(s). CoRR, (2020)Exploring machine learning methods for the Star/Galaxy Separation Problem., , , , , , , , and . IJCNN, page 123-130. IEEE, (2016)Hollow carbon nanostructures obtained from hydrothermal carbonization of lignocellulosic biomass, , , , and . JOURNAL OF MATERIALS SCIENCE, 49 (2): 665-672 (2014)A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest., , , , , , , , , and 63 other author(s). CoRR, (2020)