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

Amazon.com Price:  $64.72 (as of 05/05/2019 14:20 PST- Details)

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Description

As telescopes, detectors, and computers grow ever more powerful, the volume of data on 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 state-of-the-art 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 easy methods to use quite a lot of approaches for different types and sizes of data sets. For all applications described within the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from recent astronomical surveys (as an example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly to be had, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce the entire figures and examples, evaluate the methods, and adapt them to their own fields of interest.

  • Describes essentially the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets

  • Features real-world data sets from recent astronomical surveys

  • Uses a freely to be had Python codebase throughout

  • Ideal for students and working astronomers

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