Machine Learning for Small Bodies in the Solar System

Machine Learning for Small Bodies in the Solar System is the first book devoted entirely to the application of artificial intelligence and machine learning to the study of asteroids, comets, and trans-Neptunian objects. Covering topics from celestial mechanics and asteroid family identification to object detection, spectroscopy, and autonomous data analysis, it combines methodological foundations with practical implementations, including code examples and publicly available repositories. The volume is intended both as an introduction for researchers entering the field and as a reference for experienced planetary scientists seeking to incorporate modern AI techniques into their research. As the era of data-intensive surveys led by facilities such as the Vera C. Rubin Observatory begins, the methods presented in this book provide an essential foundation for the next generation of solar system science.