Numerical Python in Astronomy and Astrophysics
A Practical Guide to Astrophysical Problem Solving
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Beschreibung
This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all set in the context of astronomy and astrophysics. Readers are guided through function design, solving differential equations, and tackling classic dynamical problems. It enables students to learn programming through practical examples and motivates independent research.
This book opens by emphasizing the growing role of computational techniques in modern astronomy and introduces Python as an ideal language for beginners. Key concepts such as Kepler’s laws and gravitational forces are used to illustrate basic calculations.
This revised and expanded book includes a new chapter that offers a comprehensive introduction to machine learning in astronomy, expanding coverage of core methodologies and real-world applications. It introduces a wide range of modern concepts and techniques, including autoencoders, recurrent neural networks, and the fundamentals of large language models, demonstrating their relevance to contemporary astrophysical research.
A major new feature of this edition is the extensive introduction of object-oriented programming throughout Chapter 4, providing readers with essential tools for developing structured, reusable, and scalable scientific software. The appendix on Python performance has been extended to include Numba, a tool for compiling Python code to machine code, demonstrated through optimization of the N-body solver. The new edition also includes new examples and exercises that deepen understanding and reflect current research challenges in astrophysics.
This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all set in the context of astronomy and astrophysics. Readers are guided through function design, solving differential equations, and tackling classic dynamical problems. It enables students to learn programming through practical examples and motivates independent research.
This book opens by emphasizing the growing role of computational techniques in modern astronomy and introduces Python as an ideal language for beginners. Key concepts such as Kepler’s laws and gravitational forces are used to illustrate basic calculations.
This revised and expanded book includes a new chapter that offers a comprehensive introduction to machine learning in astronomy, expanding coverage of core methodologies and real-world applications. It introduces a wide range of modern concepts and techniques, including autoencoders, recurrent neural networks, and the fundamentals of large language models, demonstrating their relevance to contemporary astrophysical research.
A major new feature of this edition is the extensive introduction of object-oriented programming throughout Chapter 4, providing readers with essential tools for developing structured, reusable, and scalable scientific software. The appendix on Python performance has been extended to include Numba, a tool for compiling Python code to machine code, demonstrated through optimization of the N-body solver. The new edition also includes new examples and exercises that deepen understanding and reflect current research challenges in astrophysics.
Offers an accessible approach to numerical techniques and methods of data analysis used by astrophysicists New chapter introduces machine learning with practical applications and core methodologies New examples and exercises to enrich learning in this expanded and revised edition
Autor*in
Wolfram Schmidt-Brückner
Themen in »Numerical Python in Astronomy and Astrophysics«
Undergraduate Astrophysics Textbook Astrophysical Problem Solving Programming Language Numerical Problems in Astrophysics Programming Algorithms Functions in Python Python Libraries Phyton for Astrophysics Astronomical Data Analysis Artificial Neural Networks Astronomical Image Analysis