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

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.

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

  Autorentext

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

  Inhalt

  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.