Statistics, Data Mining, and Machine Learning in Astronomy

Željko Ivezić; Andrew J. Connolly; Jacob T. VanderPlas; Alexander Gray

Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas and Alexander Gray

Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

In: Princeton Series in Modern Observational Astronomy, 1

Princeton University Press | 2014

https://doi.org/10.1515/9781400848911

Overview
Contents

About this 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.

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

Author information

Željko Ivezić 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 an NSF postdoctoral research fellow in astronomy and computer science at the University of Washington. Alexander Gray is professor of computer science at Georgia Institute of Technology.

Reviews

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

"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

Ivezic and colleagues at the University of Washington and the Georgia Institute of Technology have written a comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics. . . . The authors provide another valuable service by discussing how to access data from key astronomical research programs.

Winner of the 2016 IAA Outstanding Publication Award, International Astrostatistics Association

"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

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

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Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

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