Where to look for more information¶
This book can’t possibly cover everything there is to know in two massive disciplines. Each author here has obtained their knowledge through other references and through experience. The latter is hard earned but we can easily share some of the former.
The Cambridge Encyclopedia of the Sun by Kenneth R. Lang
An excellent overview of the basics of the Sun.
Journey from the Center of the Sun by Jack B. Zirker
A good start for pretty much anyone; covers the physics from the center of the Sun to its influence on Earth.
The Sun, the Earth, and Near-Earth Space by Jack Eddy
A well-written book covering most of heliophysics and its place in astronomy without an overwhelming amount of technical detail.
Severe Space Weather Events - Understanding Societal and Economic Impacts by the National Research Council
A great reference for why heliophysics matters in a practical sense.
The Solar Transition Region by John T. Mariska
A deep dive into the physics of a specific layer of the solar corona called the transition region.
Stellar Atmospheres by Dimitri Mihalas
A deep dive into the physics of stellar atmospheres.
Machine Learning and Data Science¶
Statistics, Data Mining, and Machine Learning in Astronomy by Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas & Alexander Gray
This book, which also has an accompanying python module called AstroML, introduces the reader to sophisticated statistics, data mining, and machine learning techniques to analyze large data sets in astrophysics. Heliophysics can be be considered a specialized subset of astrophysics, so many of the methods described in this book are relevant.
Python Data Science Handbook by Jacob T. VanderPlas
A good way to get started with the core libraries necessary for scientific computing. The final chapter is dedicated to machine learning with the python package
Machine Learning via Coursera by Andrew Ng
This online course provides a broad, practical introduction machine learning techniques by going through weekly lectures and coding homeworks (based on the Stanford University course CS 229).
A (seemingly) endless list of notebooks that both introduce machine learning at a beginner’s level and work through practical examples (John Wittenauer’s collection is especially impressive).