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The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback., , , , , , , , , and 1 other author(s). CoRR, (2022)First direct astrophysical constraints on dark matter interactions with ordinary matter at very low velocities, and . (2019)cite arxiv:1903.12190Comment: 11 pages, 8 figures.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.The CAMELS project: public data release., , , , , , , , , and 37 other author(s). CoRR, (2022)A new approach to template banks of gravitational waves with higher harmonics: reducing matched-filtering cost by over an order of magnitude., , , , , , , and . CoRR, (2023)The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence., , , , , , , , , and 18 other author(s). CoRR, (2021)Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter., , , , , , , , , and . CoRR, (2022)Percent-level constraints on baryonic feedback with spectral distortion measurements, , , , , , , , , and . (2022)cite arxiv:2201.01663Comment: 10+5 pages, 7 figures.