Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Željko Ivezic, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray


Book Details:

ISBN: 0691151687
EAN: 9780691151687
ASIN: 0691151687
Publisher: Princeton collage Press
Publication Date: 2014-01-12
Number of Pages: 560
Website: Amazon, LibraryThing, Google Books, Goodreads

Synopsis from Amazon:

As telescopes, detectors, and desktops develop ever extra robust, the amount of information on the disposal of astronomers and astrophysicists will input the petabyte area, delivering actual measurements for billions of celestial gadgets. This ebook presents a finished and available advent to the state-of-the-art statistical equipment had to successfully learn complicated info units from astronomical surveys equivalent to the Panoramic Survey Telescope and swift reaction procedure, the darkish power Survey, and the impending huge Synoptic Survey Telescope. It serves as a pragmatic instruction manual for graduate scholars and complicated undergraduates in physics and astronomy, and as an imperative reference for researchers.

Statistics, info Mining, and laptop studying in Astronomy offers a wealth of functional research difficulties, evaluates thoughts for fixing them, and explains the best way to use numerous ways for various kinds and sizes of knowledge units. For all functions defined within the ebook, Python code and instance facts units are supplied. The helping facts units were conscientiously chosen from modern astronomical surveys (for instance, the Sloan electronic Sky Survey) and are effortless to obtain and use. The accompanying Python code is publicly to be had, good documented, and follows uniform coding criteria. jointly, the information units and code let readers to breed all of the figures and examples, review the tools, and adapt them to their very own fields of interest.

  • Describes the main worthy statistical and data-mining tools for extracting wisdom from large and intricate astronomical info sets
  • Features real-world facts units from modern astronomical surveys
  • Uses a freely to be had Python codebase throughout
  • Ideal for college kids and dealing astronomers

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