Machine Learning for Small Bodies in the Solar System

Erscheinungsjahr 2024
ISBN: 978-0-443-24770-5
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 540 g

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, and detection algorithms. Offering a practical approach, the book encompasses a wide range of topics, providing both readers with essential tools and insights for use in researching asteroids, comets, moons, and Trans-Neptunian objects. 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 in 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 in their work.

Autoren/Hrsg.

Smirnov, Evgeny

Oszkiewicz, Dagmara

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Weltraumforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Astronomie Sonnensystem: Sonne und Planeten
• Naturwissenschaften Astronomie Astronomische Beobachtung: Observatorien, Instrumente, Methoden

Inhaltsverzeichnis

- Artificial intelligence and machine learning methods in celestial mechanics
- Identification of asteroid families’ members
- Asteroids inmean-motion resonances
- Asteroid families interacting with secular resonances
- Neural networks in celestial dynamics: capabilities, advantages, and challenges in orbital dynamics around asteroids
- Asteroid spectro-photometric characterization
- Machine learning-assisted dynamical classification of trans-Neptunian objects
- Identification and localization of cometary activity in Solar System objects withmachine learning
- Detectingmoving objects with machine learning
- Chaotic dynamics
- Conclusions and future developments

Über Autor(innen)

Smirnov, Evgeny
Dr. Evgeny Smirnov works in the field of the dynamics of asteroids. In 2017, he introduced a machine learning approach based on the supervised learning for the identification procedure that decreases the computational time from weeks to seconds. In the same year, he proposed a similar approach for asteroid families instead of the classical HCM method. With a strong background in science and software development, Evgeny connects these areas and brings modern software development patterns and techniques into the field of astronomy.

Oszkiewicz, Dagmara
Prof. Dagmara Oszkiewicz is a Polish astronomer and planetary scientist. She is an assistant professor at Adam Mickiewicz University in Poznan, Poland, where her research focuses on physical and orbital properties of small Solar System bodies. She has recently expanded her research to include machine learning techniques to the analysis of asteroid spectro-photometric data; her latest work includes applications of machine learning algorithms to the classification of basaltic asteroids in the context of formation of differentiated planetesimals and comparison of various machine learning algorithms for classification of spectro-photometric data from various large sky surveys.