Machine learning for observational cosmology

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.

Automated summary

In the next decade, a series of large observational programs using ground-based and space-borne telescopes are planned. These programs are expected to generate an unprecedented amount of data, exceeding an exabyte. Processing such massive astronomical data poses technical challenges and calls for urgently needed fully automated technologies based on machine learning and artificial intelligence. Maximizing the scientific potential of big data requires collaborative efforts from the scientific community. In this article, we summarize the recent progress in the application of machine learning in observational cosmology, as well as address crucial issues in high-performance computing for data processing and statistical analysis.