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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.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.Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning, , , , , , , , , and 3 other author(s). (2009)cite arxiv:0908.2033Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS. Revised to match accepted version..Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects., , , , , , , , , and 49 other author(s). CoRR, (2020)LSTM and CNN application for core-collapse supernova search in gravitational wave real data., , , , and . CoRR, (2023)A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest., , , , , , , , , and 63 other author(s). CoRR, (2020)Dark Energy, Paradigm Shifts, and the Role of Evidence, and . (May 3, 2014)Upper Bound of Neutrino Masses from Combined Cosmological Observations and Particle Physics Experiments, , , , , , , , , and 2 other author(s). (2018)cite arxiv:1811.02578Comment: 5 pages, 2 figures.Primordial Non-Gaussianity, , , , , , , , , and 168 other author(s). (2019)cite arxiv:1903.04409Comment: 5 pages + references; Submitted to the Astro2020 call for science white papers. This version: fixed author list.Galaxy Zoo: Reproducing Galaxy Morphologies via Machine Learning, , , , , , , , , and 3 other author(s). Monthly Notices of the Royal Astronomical Society, 406 (1): 342--353 (2010)