太陽系における小天体のための機械学習 Machine Learning for Small Bodies in the Solar System

Carruba, Valerio (EDT)/ Smirnov, Evgeny (EDT)/ Oszkiewicz, Dagmara (EDT)

Elsevier - Health Sciences Division（2024/11発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 328 p.
• 言語 ENG
• 商品コード 9780443247705
• DDC分類 523.20285631

Full Description

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.

Contents

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