Machine Learning for Small Bodies in the Solar System
Language: English
Published by Elsevier Inc Nov 2024, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9780443247705
- Title
- Machine Learning for Small Bodies in the Solar System
- Author
- Valerio Carruba
- Publisher
- Elsevier Inc Nov 2024
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443247706
- ISBN 13
- 9780443247705
- Item weight
- 447 grams
- Dimensions
- 229x152x18 mm
- Provides a practical reference to applications of machine learning and artificial intelligence to small bodies in the Solar System
- Approaches the topic from a multidisciplinary perspective, with chapters on dynamics, physical properties and software development
- Includes code and links to publicly available repositories to allow readers practice the methodology covered
"Synopsis" may belong to another edition of this title.
About the Author
Prof. Valerio Carruba is currently an Associate Professor at the São Paulo State University (UNESP) in Brazil. He is one of the founders of the Machine Learning applied to Small Bodies (MASB) research group. His recent interests involve the use of deep learning for the identification of asteroids in secular resonant configurations and machine learning applied for asteroid families’ identification. Asteroid 10741 has been named “Valeriocarruba by the International Astronomical Union. His paper “Optimization of artificial neural networks models applied to the identification of images of asteroids’ resonant arguments recently won the CELMEC prize for "Innovative computational methods in Dynamical Astronomy".
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.
Prof. Dagmara Oszkiewicz is a Polish astronomer and planetary scientist. She is an assistant professor at Adam Mickiewicz University in Poznań, 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.
"About the title" may belong to another edition of this title.
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