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Fast likelihood-free cosmology with neural density estimators and active learning

, , , and . (2019)cite arxiv:1903.00007Comment: Submitted to MNRAS Feb 2019.
DOI: 10.1093/mnras/stz1960

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Super-resolution emulator of cosmological simulations using deep physical models, , , and . (2020)cite arxiv:2001.05519Comment: 10 pages, 9 figures. For submission to MNRAS.The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues, , , , , and . (2022)cite arxiv:2207.05202Comment: 16 pages, 10 figures. To be submitted to RASTI. We provide code and a tutorial for the analysis and relevant software at https://github.com/tlmakinen/cosmicGraphs.Hybrid summary statistics: neural weak lensing inference beyond the power spectrum., , , , , and . CoRR, (2024)Fast likelihood-free cosmology with neural density estimators and active learning, , , and . (2019)cite arxiv:1903.00007Comment: Submitted to MNRAS Feb 2019.The Quijote simulations, , , , , , , , , and 17 other author(s). (2019)cite arxiv:1909.05273Comment: 19 pages, 15 figures. Simulations publicly available at https://github.com/franciscovillaescusa/Quijote-simulations.