Artificial Intelligence and Machine Learning in Astronomy

Abstract

This chapter examines the advancements, challenges, and limitations of Artificial Intelligence (AI) and Machine Learning (ML) in the study of the cosmos. The rapid expansion of high-volume astronomical datasets has necessitated the development of advanced computational frameworks for efficient data processing and analysis. Astronomy, regarded as the oldest scientific discipline, has evolved significantly from its mythological origins to the modern era. In contrast, AI is a contemporary field characterized by its problem-solving capabilities, language processing, and learning properties. The application of AI is increasingly essential in astronomy. Supervised, Unsupervised, and deep learning models have demonstrated strong performance in galaxy classification, exoplanet detection, and gravitational wave signal extraction. These models have transformed the field by enabling more accurate and efficient detection. Despite these advancements, significant challenges remain. This chapter discusses these challenges alongside potential solutions, contributing to the on-going integration of AI and ML within astronomy.