Applications of Big Data and Machine Learning in Galaxy Formation and Evolution

Tsutomu T. Takeuchi

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Hardback

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English

CRC Press
29 April 2025

Mathematics; Statistical physics; Astrophysics; Automatic control engineering; Machine learning

Series: Series in Astronomy and Astrophysics

As investigations into our Universe become more complex, in-depth, and widespread, galaxy surveys are requiring state-of-the-art data scientific methods to analyze them. This book provides a practical introduction to big data in galaxy formation and evolution, introducing the astrophysical basics, before delving into the latest techniques being introduced to astronomy and astrophysics from data science. This book helps translate the cutting-edge methods into accessible guidance for those without a formal background in computer science. It is an ideal manual for astronomers and astrophysicists, in addition to graduate students and postgraduate students in science and engineering looking to learn how to apply data-science to their research.

Key Features:

Introduces applications of data-science methods to the exciting subject of galaxy formation and evolution Provides a practical guide to understanding cutting-edge data-scientific methods, as well as classical astrostatistical methods Summarises a vast range of statistical and informatics methods in one volume, with concrete applications to astrophysics

By: Tsutomu T. Takeuchi
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 940g
ISBN: 9780367611392
ISBN 10: 0367611392
Series: Series in Astronomy and Astrophysics
Pages: 402
Publication Date: 29 April 2025
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Hardback
Publisher's Status: Active

Tsutomu T. Takeuchi is Associate Professor, Division of Particle and Astrophysical Science, Nagoya University, Japan.

Reviews for Applications of Big Data and Machine Learning in Galaxy Formation and Evolution

This book is an outstanding fusion of galactic astronomy and modern statistical analysis, including machine learning. The first half concisely covers fundamental processes such as radiation and gas dynamics, along with a wide range of galactic phenomena. The second half provides numerous practical examples, including both supervised methods like convolutional neural networks, as well as a strong emphasis on unsupervised techniques such as principal component analysis, VAE, and UMAP. Additionally, it explores statistical methods like copulas and advanced approaches such as topological data analysis, making it an indispensable resource for the big data era in astronomy. Prof. Takeuchi, a pioneer in applying statistical methods to astronomy, has uniquely positioned this book at the intersection of galactic studies and modern data science. For graduate students eager to bridge these fields, this book eliminates the need for multiple textbooks, offering a singular, authoritative guide. - Makoto Uemura, Hiroshima University, April 2025