Description
Machine Learning in Astronomy (IAU S368) presents cutting-edge insights from the International Astronomical Union symposium on applying artificial intelligence and machine learning to astronomical research. This comprehensive proceedings volume features contributions from leading astronomers and data scientists, including editors Jess McIver, Ashish Mahabal, and Christopher Fluke.
The book provides a balanced examination of how machine learning is transforming astronomy, from automated discovery of celestial objects to pattern recognition in massive datasets. Contributors explore practical applications in survey astronomy, time-series analysis, and image processing, while also addressing important challenges such as bias in training data, interpretability of algorithms, and reproducibility concerns.
Essential reading for astronomers, data scientists, and researchers seeking to understand both the opportunities and limitations of machine learning in modern astronomical science.
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