Machine Learning for Small Bodies in the Solar System

Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, detection algorithms, etc. Allowing readers to apply ML and AI to the study of asteroids, comets, moons, and Trans-Neptunian Objects. The practical approach encompasses a wide range of topics, providing both experienced and novice researchers with essential tools and insights. The inclusion of codes and links to publicly available repositories further facilitates hands-on learning, enabling readers to put their newfound knowledge into practice. Machine Learning for Small Bodies in the Solar System serves as an invaluable reference for researchers working into the broad fields of Solar System bodies; both seasoned researchers seeking to enhance their understanding of ML and AI in the context of Solar System exploration or those just stepping into the field looking for direction on Methodologies and techniques to apply ML and AI methodologies. Provides a practical reference to applications of machine learning and artificial intelligence to small bodies in the Solar SystemApproaches the topic from a multi-disciplinary perspective, with chapters on dynamics, physical properties and software developmentIncludes code and links to publicly available repositories to allow readers to practice the methodology covered INDICE: 1. Machine Learning and Artificial Intelligence, an Overview2. Identification of Asteroid Families’ Members3. Asteroids in Mean-Motion Resonances4. Asteroid Families Interacting with Secular Resonances5. Orbital Dynamics Around Asteroids6. Asteroid Spectro-Photometric Classification7. Kuiper Belt Objects8. Identification and Localization of cometary activity in Solar System Objects with Machine Learning9. Machine Learning for Classifying Meteorites10. Detection and Characterization of Moving Objects with Machine Learning11. Chaotic dynamics12. Conclusions and Future Developments

• ISBN: 978-0-443-24770-5
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/01/2025
• Nº Volúmenes: 1
• Idioma: Inglés