Statistics, data mining, and machine learning in astronomy: a practical python guide for the analysis of survey data

Author(s)

• Ivezić, Željko
• Connolly, Andrew J.
• Vanderplas, Jacob T.
• Gray, Alexander

Bibliographic Information

Statistics, data mining, and machine learning in astronomy: a practical python guide for the analysis of survey data

Željko Ivezić ... [et al.]

（Princeton series in modern observational astronomy）

Princeton University Press, c2014

Note

Includes index

Other editors: Andrew J. Connolly, Jacob T. Vanderplas, Alexander Gray

Description and Table of Contents

Description

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. 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. * 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 contemporary astronomical surveys * Uses a freely available Python codebase throughout * Ideal for students and working astronomers

Table of Contents

*Frontmatter, pg. i*Contents, pg. v*Preface, pg. ix*1. About the Book and Supporting Material, pg. 3*2. Fast Computation on Massive Data Sets, pg. 43*3. Probability and Statistical Distributions, pg. 69*4. Classical Statistical Inference, pg. 123*5. Bayesian Statistical Inference, pg. 175*6. Searching for Structure in Point Data, pg. 249*7. Dimensionality and Its Reduction, pg. 289*8. Regression and Model Fitting, pg. 321*9. Classification, pg. 365*10. Time Series Analysis, pg. 403*A. An Introduction to Scientific Computing with Python, pg. 471*B. AstroML: Machine Learning for Astronomy, pg. 511*C. Astronomical Flux Measurements and Magnitudes, pg. 515*D. SQL Query for Downloading SDSS Data, pg. 519*E. Approximating the Fourier Transform with the FFT, pg. 521*Visual Figure Index, pg. 527*Index, pg. 533

by "Nielsen BookData"

Details

• NCID: BB14796226
• ISBN:
  • 9780691151687
• LCCN: 2013951369
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Princeton, N.J.
• Pages/Volumes: x, 540 p., [8] p. plates
• Size: 27 cm
• Classification:
  • LCC : QB51.3.E43
  • DC23 : 522/.8555133
• Subject Headings:
  • LCSH : Astronomy -- Data processing
  • LCSH : Statistical astronomy
• Parent Bibliography ID:
  • BB14795110