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

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

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 2025Professor Takeuchi is a well-known researcher in extragalactic astrophysics and a recognized expert in statistical methods applied to this field. His expertise is so widely respected that even fellow astronomers frequently consult him on statistical questions related to astrophysics.This book offers a comprehensive overview of galaxies and extragalactic astrophysics. As a teacher of astrophysics, I would strongly recommend it to students seeking a deeper understanding of galaxies and the physical equations that govern them across all redshifts. Beyond its solid theoretical foundation, the second part of the book delves into the application of statistics and data science in the study of galaxies. Readers will learn how to derive key physical and cosmological parameters necessary from data analysis and for gaining insights into galaxy evolution. It is an essential resource not only for students in extragalactic astrophysics but also for scientists interested in integrating data-science techniques into their research.- Denis Burgarella, Laboratoire d'astrophysique de Marseille, May 2025

• Chapter 1: Introduction. Chapter 2: Properties of Galaxies. Chapter 3: Interstellar Medium (ISM). Chapter 4: Chemical Evolution of Galaxies. Chapter 5: Observational Star Formation Rate Indicator. Chapter 6: Clusters, Clustering of Galaxies, and the Large-Scale Structure. Chapter 7: Structure and Galaxy Formation in the Universe. Chapter 8: Basics of Statistics. Chapter 9: Expectation-Maximization (EM) Algorithm. Chapter 10: Copula and Luminosity and Mass Functions of Galaxies. Chapter 11: High-dimensional Statistical Analysis. Chapter 12: Basics of Machine Learning. Chapter 13: Galaxy Face. Chapter 14: New Quantification of Galaxy Evolution by Manifold Learning. Chapter 15: Topological Data Anlysis of the Large-Scale Structure. Chapter 16: Radio Morphology of Galaxies with Machine Learning. Appendix A: Cosmological Basics. Appendix B: Supplementary Information on Mathematics and Machine Learning. Appendix C: Physical Constants and Units. Bibliography. Index.