Limited Offer
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Machine Learning for Small Bodies in the Solar System
- 1st Edition - January 1, 2025
- Editors: Valerio Carruba, Evgeny Smirnov, Dagmara Oszkiewicz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 7 7 0 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 7 7 1 - 2
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 dif… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteMachine 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 for 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 to practice the methodology covered
Researchers and practitioners in the fields of planetary science, astrostatistics, celestial mechanics, and observational astronomy
1. Machine Learning and Artificial Intelligence, an Overview
2. Identification of Asteroid Families’ Members
3. Asteroids in Mean-Motion Resonances
4. Asteroid Families Interacting with Secular Resonances
5. Orbital Dynamics Around Asteroids
6. Asteroid Spectro-Photometric Classification
7. Kuiper Belt Objects
8. Identification and Localization of cometary activity in Solar System Objects with Machine Learning
9. Machine Learning for Classifying Meteorites
10. Detection and Characterization of Moving Objects with Machine Learning
11. Chaotic dynamics
12. Conclusions and Future Developments
2. Identification of Asteroid Families’ Members
3. Asteroids in Mean-Motion Resonances
4. Asteroid Families Interacting with Secular Resonances
5. Orbital Dynamics Around Asteroids
6. Asteroid Spectro-Photometric Classification
7. Kuiper Belt Objects
8. Identification and Localization of cometary activity in Solar System Objects with Machine Learning
9. Machine Learning for Classifying Meteorites
10. Detection and Characterization of Moving Objects with Machine Learning
11. Chaotic dynamics
12. Conclusions and Future Developments
- No. of pages: 300
- Language: English
- Edition: 1
- Published: January 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443247705
- eBook ISBN: 9780443247712
VC
Valerio Carruba
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. He has published more than 70 papers, 12 of which are on applications of machine learning to dynamics of small bodies. 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 recent 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".
Affiliations and expertise
Associate Professor, São Paulo State University (UNESP), BrazilES
Evgeny Smirnov
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 to the identification procedure that decreases the computational time from weeks to seconds. The same year, he proposed a similar approach for asteroid families instead of the classical HCM method. Having a strong background in science and software development, Evgeny connects these areas and brings modern software development patterns and techniques in the field of astronomy.
Affiliations and expertise
Barcelona, SpainDO
Dagmara Oszkiewicz
Dr. Dagmara Oszkiewicz is a Polish astronomer and planetary scientist. She received her PhD from the University of Helsinki, Finland in 2012 and is currently an assistant professor at Adam Mickiewicz University in Poznań, Poland. Her research focuses on physical and orbital properties of small Solar System bodies. Recently she expanded her research to include machine learning techniques to the analysis of asteroid spectro-photometric data. Her latest works include applications of machine learning algorithms to the classification of basaltic asteroids in the context of the formation of differentiated planetesimals (planetary embryos that existed in the Solar System 4 billion years ago) and comparison of various machine learning algorithms for the classification of spectro-photometric data from various large sky surveys.
Affiliations and expertise
Assistant Professor, Adam Mickiewicz University in Poznań, Poland