Statistics, Data Mining, and Machine Learning in Astronomy - (Princeton Modern Observational Astronomy) (Hardcover)

About the Book

"As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest"--

Book Synopsis

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.

An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.

• Fully revised and expanded
  
• Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets
  
• Features real-world data sets from astronomical surveys
  
• Uses a freely available Python codebase throughout
  
• Ideal for graduate students, advanced undergraduates, and working astronomers
  

Review Quotes

Praise for the previous edition:

"A comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics."--Choice

"A substantial work that can be of value to students and scientists interested in mining the vast amount of astronomical data collected to date. . . . If data mining and machine learning fall within your interest area, this text deserves a place on your shelf."--Planetarian

"This comprehensive book is surely going to be regarded as one of the foremost texts in the new discipline of astrostatistics."--Joseph M. Hilbe, president of the International Astrostatistics Association

"In the era of data-driven science, many students and researchers have faced a barrier to entry. Until now, they have lacked an effective tutorial introduction to the array of tools and code for data mining and statistical analysis. The comprehensive overview of techniques provided in this book, accompanied by a Python toolbox, free readers to explore and analyze the data rather than reinvent the wheel."--Tony Tyson, University of California, Davis

"The authors are leading experts in the field who have utilized the techniques described here in their own very successful research. Statistics, Data Mining, and Machine Learning in Astronomy is a book that will become a key resource for the astronomy community."--Robert J. Hanisch, Space Telescope Science Institute

About the Author

Zeljko Ivezic is professor of astronomy at the University of Washington. Andrew J. Connolly is professor of astronomy at the University of Washington. Jacob T. VanderPlas is a software engineer at Google. Alexander Gray is vice president of AI science at IBM.