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DES science portal: Computing photometric redshifts., , , , , , , , , и 60 other автор(ы). Astron. Comput., (2018)Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era, , , , , , , , , и 29 other автор(ы). (2021)cite arxiv:2110.03769Comment: 46 pages, 33 figures.A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest., , , , , , , , , и 63 other автор(ы). CoRR, (2020)Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering., , , , , , , , , и 1 other автор(ы). Astroinformatics, том 12 из Proceedings of the International Astronomical Union, стр. 247-252. Cambridge University Press, (2016)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.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)