Statistics, Data Mining, and Machine Learning in Astronomy

As telescopes, detectors, and computers grow ever more powerful, the volume ofdata at the disposal of astronomers and astrophysicists will enter thepetabyte domain, providing accurate measurements for billions of celestialobjects. This book provides a comprehensive and accessible introduction to thecutting-edge statistical methods needed to efficiently analyze complex datasets from astronomical surveys such as the Panoramic Survey Telescope andRapid Response System, the Dark Energy Survey, and the upcoming Large SynopticSurvey Telescope. It serves as a practical handbook for graduate students andadvanced undergraduates in physics and astronomy, and as an indispensablereference for researchers.

"Statistics, Data Mining, and Machine Learning in Astronomy" presents a wealthof practical analysis problems, evaluates techniques for solving them, andexplains how to use various approaches for different types and sizes of datasets. For all applications described in the book, Python code and example datasets are provided. The supporting data sets have been carefully selected fromcontemporary astronomical surveys (for example, the Sloan Digital Sky Survey)and are easy to download and use. The accompanying Python code is publiclyavailable, well documented, and follows uniform coding standards. Together,the data sets and code enable readers to reproduce all the figures andexamples, evaluate the methods, and adapt them to their own fields ofinterest. Describes the most useful statistical and data-mining methods forextracting knowledge from huge and complex astronomical data sets Featuresreal-world data sets from contemporary astronomical surveys Uses a freelyavailable Python codebase throughout Ideal for students and workingastronomers