# Statistics, Data Mining, and Machine Learning in Astronomy

## Book Description

**Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy)**

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

Part I Introduction

Chapter 1 About the Book and Supporting Material

Chapter 2 Fast Computation on Massive Data Sets

Part II Statistical Frameworks and Exploratory Data Analysis

Chapter 3 Probability and Statistical Distributions

Chapter 4 Classical Statistical Inference

Chapter 5 Bayesian Statistical Inference

Part III Data Mining and Machine Learning

Chapter 6 Searching for Structure in Point Data

Chapter 7 Dimensionality and Its Reduction

Chapter 8 Regression and Model Fitting

Chapter 9 Classification

Chapter 10 Time Series Analysis

Part IV Appendices

Appendix A An Introduction to Scientific Computing with Python

Appendix B AstroML:Machine Learning for Astronomy

Appendix C Astronomical Flux Measurements andMagnitudes

Appendix D SQL Query for Downloading SDSS Data

Appendix E Approximating the Fourier Transform with the FFT

## Book Details

- Title: Statistics, Data Mining, and Machine Learning in Astronomy
- Author: Alexander Gray, Andrew J. Connolly, Jacob T VanderPlas, Željko Ivezić
- Length: 552 pages
- Edition: 1
- Language: English
- Publisher: Princeton University Press
- Publication Date: 2014-01-12
- ISBN-10: 0691151687
- ISBN-13: 9780691151687

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