Numerical Python in Astronomy and Astrophysics: A Practical Guide to Astrophysical Problem Solving (Undergraduate Lecture Notes in Physics)

✍ Scribed by Wolfram Schmidt, Marcel Völschow

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 257
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler’s laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks.

✦ Table of Contents

Preface
Acknowledgements
Contents
1 Python Basics
1.1 Using Python
1.2 Understanding Expressions and Assignments
1.3 Control Structures
1.4 Working with Modules and Objects
2 Computing and Displaying Data
2.1 Spherical Astronomy
2.1.1 Declination of the Sun
2.1.2 Diurnal Arc
2.1.3 Observation of Celestial Objects
2.2 Kepler's Laws of Planetary Motion
2.3 Tidal Forces
3 Functions and Numerical Methods
3.1 Blackbody Radiation and Stellar Properties
3.1.1 Stefan–Boltzmann Law
3.1.2 Planck Spectrum
3.2 Physics of Stellar Atmospheres
3.2.1 Thermal Excitation and Ionization
3.2.2 The Balmer Jump
3.3 Planetary Ephemerides
4 Solving Differential Equations
4.1 Numerical Integration of Initial Value Problems
4.1.1 First Order Differential Equations
4.1.2 Second Order Differential Equations
4.2 Radial Fall
4.3 Orbital Mechanics
4.4 Galaxy Collisions
4.5 Stellar Clusters
4.6 Expansion of the Universe
5 Astronomical Data Analysis
5.1 Spectral Analysis
5.2 Transit Light Curves
5.3 Survey Data Sets
5.4 Image Processing
5.5 Machine Learning
5.5.1 Image Classification
5.5.2 Spectral Classification
Appendix A Object-Oriented Programming in a Nutshell
Appendix B Making Python Faster
B.1 Using Arrays
B.2 Cythonizing Code
B.3 Parallelization and GPU Offloading
Appendix References
Index