Applications of Big Data and Machine Learning in Galaxy Formation and Evolution [Hardcover]

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.

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.

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

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 astrol)