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MApping the Most Massive Overdensities Through Hydrogen (MAMMOTH) I: Methodology

, , , , , , , , , , and . (2015)cite arxiv:1512.06859Comment: 24 pages, 30 figures, 8 tables, submitted to the Astrophysical Journal.

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Search for CII Emission on Cosmological Scales at Redshift Z~2.6, , , , and . (2017)cite arxiv:1707.06172Comment: 14 pages, 9 figures, to be submitted to MNRAS.From Dark Matter to Galaxies with Convolutional Networks., , , , , , , and . CoRR, (2019)Rediscovering orbital mechanics with machine learning., , , , and . CoRR, (2022)Multiple Physics Pretraining for Physical Surrogate Models., , , , , , , , , and 4 other author(s). CoRR, (2023)The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological implications of the full shape of the clustering wedges in the data release 10 and 11 galaxy samples, , , , , , , , , and 18 other author(s). (2013)cite arxiv:1312.4854Comment: 24 pages, 14 figures. Submitted to MNRAS. Measurements and covariance matrices are available at https://sdss3.org/science/boss_publications.php.Learning to Predict the Cosmological Structure Formation., , , , , , and . CoRR, (2018)HInet: Generating neutral hydrogen from dark matter with neural networks, , , and . (2020)cite arxiv:2007.10340Comment: 13 pages, 8 figures.The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations, , , , , , , , , and 11 other author(s). (2020)cite arxiv:2010.00619Comment: 33 pages, 18 figures, CAMELS webpage at https://www.camel-simulations.org.Using the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure, , , , and . (2020)cite arxiv:2001.11024Comment: 5 pages, 3 figures.Fast Function to Function Regression., , , , , , and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)