摘要
Exoplanets, which are planets orbiting stars outside our solar system, have intrigued both the scientific community and the public due to the possibility of uncovering diverse and distant worlds far beyond our own. As of 17 October 2024, 5,780 exoplanets have been confirmed across 4,314 planetary systems, with 969 of these systems containing multiple planets. In the pursuit of detecting and characterizing these distant objects, astronomers employ a range of advanced techniques and technologies, with the transit method playing a central role. This technique, which has been instrumental in identifying over 90% of known exoplanets, involves detecting periodic reductions in a star’s brightness, as a planet transits across its face. In this chapter, titled The Role of Artificial Intelligence in the Discovery and Characterization of Exoplanets, we first provide a comprehensive overview of the different types of exoplanets. We then explore the primary methods used in exoplanet detection, including transit photometry, radial velocity, direct imaging, gravitational microlensing, pulsar timing, and astrometry. In recent years, the integration of artificial intelligence (AI) has significantly enhanced our ability to identify, analyze, and interpret the data associated with exoplanet discovery and characterization. AI, particularly through machine learning (ML) algorithms, has refined the analysis of complex datasets, enabling more efficient identification of exoplanet candidates and uncovering previously elusive planets. Machine learning models, trained on existing data, can recognize the subtle signatures of exoplanets, allowing researchers to rapidly identify potential candidates for further study. Moreover, AI can process vast datasets from space missions such as Kepler and TESS more effectively than traditional methods, accelerating the discovery of new exoplanets. In addition to improving detection capabilities, AI is also pivotal in the characterization of exoplanets. Once an exoplanet is identified, understanding its physical properties, such as its composition, size, atmosphere, and potential for habitability, requires the analysis of complex spectral data and other observational measurements. The AI-driven approaches are particularly valuable in the search for Earth-like exoplanets, where precise predictions of habitability are crucial for directing future missions aimed at detecting signs of life. The application of AI and ML has significantly enhanced detection techniques, enabling more efficient analysis of complex data and revealing previously overlooked planets. Looking to the future, AI is expected to remain a driving force in exoplanet research, especially with the emergence of next-generation space telescopes, such as ESA’s PLAnetary Transits and Oscillations of stars (PLATO) space mission and NASA’s Nancy Grace Roman Space Telescope (RST). These missions will generate unprecedented amounts of data, further necessitating the use of AI for data processing, analysis, and interpretation. The continued integration of AI into exoplanet science will accelerate the pace of discoveries and open new avenues for exploring the broader cosmic context of exoplanetary systems. This chapter is intended to be accessible to both experts and general readers, illustrating how AI is transforming the field of exoplanet research. As AI technology evolves, it promises to further deepen our understanding of the universe and may ultimately help answer the profound question: Are we alone in the cosmos?. © 2025 Scrivener Publishing LLC.