%0 Generic %1 moriwaki2023machine %A Moriwaki, Kana %A Nishimichi, Takahiro %A Yoshida, Naoki %D 2023 %K bayesian_analysis cosmology machine_learning review %T Machine Learning for Observational Cosmology %U http://arxiv.org/abs/2303.15794 %X An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires community-wide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

@misc{moriwaki2023machine, abstract = {An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires community-wide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.}, added-at = {2023-03-29T14:40:13.000+0200}, author = {Moriwaki, Kana and Nishimichi, Takahiro and Yoshida, Naoki}, biburl = {https://www.bibsonomy.org/bibtex/23024004b6cab2c3449265fc826be9bea/intfxdx}, description = {Machine Learning for Observational Cosmology}, interhash = {2de73238e64a8944f949eb3577f4f640}, intrahash = {3024004b6cab2c3449265fc826be9bea}, keywords = {bayesian_analysis cosmology machine_learning review}, note = {cite arxiv:2303.15794Comment: 55 pages, 8 figures, submitted to Reports on Progress in Physics}, timestamp = {2025-01-13T10:33:25.000+0100}, title = {Machine Learning for Observational Cosmology}, url = {http://arxiv.org/abs/2303.15794}, year = 2023 }