Applications of AI in Astronomy


We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.


DOI: 10.1142/13123

Projects: Astroinformatics

Publication type: InCollection

Book Title: Artificial Intelligence for Science

Editors: Alok Choudhary, Geoffrey Fox , Tony Hey



Date Published: 1st Apr 2023


Registered Mode: manually

Authors: S. G. Djorgovski, Ashish Mahabal, M. J. Graham, Kai L. Polsterer, Alberto Krone-Martins

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Choudhary, A., Fox, G., & Hey, T. (2022). Artificial Intelligence for Science. In []. WORLD SCIENTIFIC.

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Created: 17th Feb 2023 at 15:45

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