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Numerical Python in Astronomy and Astrophysics: A Practical Guide to Astrophysical Problem Solving 1st ed. 2021 [Pehme köide]

  • Formaat: Paperback / softback, 250 pages, kõrgus x laius: 235x155 mm, kaal: 403 g, 50 Illustrations, color; 5 Illustrations, black and white; X, 250 p. 55 illus., 50 illus. in color., 1 Paperback / softback
  • Sari: Undergraduate Lecture Notes in Physics
  • Ilmumisaeg: 15-Jul-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030703460
  • ISBN-13: 9783030703462
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  • Formaat: Paperback / softback, 250 pages, kõrgus x laius: 235x155 mm, kaal: 403 g, 50 Illustrations, color; 5 Illustrations, black and white; X, 250 p. 55 illus., 50 illus. in color., 1 Paperback / softback
  • Sari: Undergraduate Lecture Notes in Physics
  • Ilmumisaeg: 15-Jul-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030703460
  • ISBN-13: 9783030703462
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.
1 Python Basics
1(18)
1.1 Using Python
1(2)
1.2 Understanding Expressions and Assignments
3(5)
1.3 Control Structures
8(6)
1.4 Working with Modules and Objects
14(5)
2 Computing and Displaying Data
19(36)
2.1 Spherical Astronomy
19(23)
2.1.1 Declination of the Sun
20(7)
2.1.2 Diurnal Arc
27(8)
2.1.3 Observation of Celestial Objects
35(7)
2.2 Kepler's Laws of Planetary Motion
42(5)
2.3 Tidal Forces
47(8)
3 Functions and Numerical Methods
55(50)
3.1 Blackbody Radiation and Stellar Properties
55(22)
3.1.1 Stefan--Boltzmann Law
56(6)
3.1.2 Planck Spectrum
62(15)
3.2 Physics of Stellar Atmospheres
77(17)
3.2.1 Thermal Excitation and Ionization
78(6)
3.2.2 The Balmer Jump
84(10)
3.3 Planetary Ephemerides
94(11)
4 Solving Differential Equations
105(80)
4.1 Numerical Integration of Initial Value Problems
105(21)
4.1.1 First Order Differential Equations
105(11)
4.1.2 Second Order Differential Equations
116(10)
4.2 Radial Fall
126(10)
4.3 Orbital Mechanics
136(15)
4.4 Galaxy Collisions
151(16)
4.5 Stellar Clusters
167(8)
4.6 Expansion of the Universe
175(10)
5 Astronomical Data Analysis
185(40)
5.1 Spectral Analysis
185(3)
5.2 Transit Light Curves
188(6)
5.3 Survey Data Sets
194(8)
5.4 Image Processing
202(5)
5.5 Machine Learning
207(18)
5.5.1 Image Classification
208(8)
5.5.2 Spectral Classification
216(9)
Appendix A Object-Oriented Programming in a Nutshell 225(8)
Appendix B Making Python Faster 233(10)
References 243(2)
Index 245
Wolfram Schmidt is 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 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 in the areas of computational cosmology and magnetohydrodynamic turbulence with international collaborators. In addition to his research activities, he teaches high-performance computing at Hamburg University and acts as a consultant to the Northern Supercomputing Alliance (HLRN).





Marcel Völschow is a research associate at Hamburg University. He holds BSc and MSc degrees in Physics and the subject of his doctorate is magnetic processes in binary stars. While being rooted in theoretical astrophysics, his research also covers the processing and analysis of large survey data sets. Whether working in public outreach, high school labs, or academic teaching, he has always been motivated by enthusiasm for the beauty of the universe. He is the author of a number of articles in peer-reviewed journals.