Machine Learning for Astrophysics
Proceedings of the ML4Astro International Conference 30 May - 1 Jun 2022
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Beschreibung
This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
Provides a comprehensive view of machine learning techniques applied to astrophysics Discusses limitations of ML applications to astrophysics With a feature on how to face future radioastronomy data deluge
Autor*in
Filomena Bufano
Themen in »Machine Learning for Astrophysics«
time series in astronomy and astrophysics anomaly discovery in data machine learning techniques software tools for machine learning deep learning data mining radioastronomy data Square Kilometer Array, SKA