Numerical Python in Astronomy and 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

Autorentext

Wolfram Schmidt-Brückner is the head of IT and senior researcher at the Hamburg Observatory. He holds a Dipl.-Ing. in Physics and an M.Phil. in Astrophysics and received his Dr. rer. nat. from the Technical University of Munich and the Max Planck Institute for Astrophysics, Garching. He completed his Habilitation with a thesis on the numerical modeling of astrophysical turbulence. Dr. Schmidt-Brückner has worked on numerical simulations of a variety of astrophysical systems, ranging from thermonuclear supernovae through star-forming clouds to cosmological structure formation. Currently, he is conducting research projects on interacting galaxies. In addition to his research activities, he teaches Python programming and numerical methods at Hamburg University.

Marcel Völschow holds a B.Sc. and an M.Sc. in Physics from the University of Hamburg and received his Dr. rer. nat. from the Hamburg Observatory, where his research focused on numerical and analytical modeling of magnetohydrodynamical processes in binary star systems. He subsequently joined the Hamburg University of Applied Sciences as a lecturer at the Faculty of Electrical Engineering, Media and Information Technology. There, he taught courses in programming, physics, and numerical methods, as well as an elective course on neural networks and data science. Currently, Dr. Völschow is a tandem researcher at DESY's Centre for X-ray and Nano Science and the Hamburg University of Applied Sciences. His work focuses on applications of emerging and edge-computing technologies to data science and numerical simulations, including neural networks, GPU computing, and quantum computing.

Inhalt
Chapter 1. Introduction.- Chapter 2. Getting Started with Python.- Chapter 3. Computing and Displaying Data.- Chapter 4. Functions and Numerical Methods.- Chapter 5. Solving Differential Equations.- Chapter 6. Astronomical Data Analysis.