Enabling New Discoveries with Machine Learning

Abstract

The next generation of telescopes such as the Square Kilometre Array and the Vera C. Rubin Observatory will produce enormous quantities of data, too large for traditional analysis techniques. Machine learning has proven invaluable in handling massive data volumes and automating tasks traditionally done by human scientists. The question is, can machine learning also automate scientific discovery? Recent advances in the field have made this feasible but only when combined with expert human input through active learning. Astronomaly is a publicly available framework for active anomaly detection in astronomy. It learns to make recommendations of interesting sources to a user, allowing scientists to quickly uncover rare and new classes of objects in large datasets. In this paper, we review some of the discoveries made using Astronomaly, discuss the latest deep learning-based approaches and explore the critical role of the cyber-human interface in unveiling cosmic mysteries hidden in massive astronomical datasets.

arXiv commentProceedings for the IAU General Assembly 2024 (Cape Town): Focus Meeting 7, invited talk. 7 pages, 4 figure

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