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Updated: Jul 31, 2025

Bringing the Visible Universe into Focus with Robo-AO

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Published on: February 12, 2013

Machine learning for observational cosmology

Kana Moriwaki¹, Takahiro Nishimichi^2,3, Naoki Yoshida^3,4

¹Research Center for the Early Universe, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan.

Reports on Progress in Physics. Physical Society (Great Britain)

|May 5, 2023

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 (ML) and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires community-wide efforts. We summarize recent progress in ML applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

Keywords:

artificial intelligence cosmology emulation machine learning sky survey

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