The Asymptotic Giant Branch (AGB) is the late stage of the evolution of intermediate and low-mass stars and is of great importance for understanding stellar evolution, nucleosynthesis, and the chemical evolution of galaxies. This paper systematically reviews the methods for identifying AGB stars, from both traditional approaches and machine learning techniques. By integrating multi-wavelength data such as optical and infrared spectra, along with stellar evolution models, we analyze the existing methods and potential directions for improvement. We also explore the possibility of using interpretable machine learning algorithms to discover new features and applying deep learning algorithms to enhance search efficiency. With the advancement of data processing technology and the widespread application of machine learning methods, future AGB star searches will be more accurate and efficient. The increased number of discoveries, enabled by more advanced search methods, will particularly enhance our ability to reveal examples of short-lived late-stage stellar evolutionary processes.
Lu et al. (Thu,) studied this question.