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

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As telescopes, detectors, and computer systems grow a lot more powerful, the quantity of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, offering accurate measurements for vast amounts of celestial items.

Stats, Data Mining, and Machine Learning in Astronomy presents an abundance of practical analysis complications, evaluates approaches for solving them, and clarifies how exactly to use various techniques for different kinds and sizes of data pieces. It acts as a useful handbook for graduate college students and advanced undergraduates in physics and astronomy, and as an essential reference for experts. This book offers a comprehensive and available launch to the cutting-advantage statistical methods had a need to efficiently analyze complicated data units from astronomical surveys like the Panoramic Study Telescope and Fast Response Program, the Dark Energy Study, and the upcoming Huge Synoptic Study Telescope. For all applications defined in the reserve, Python code and example data units are given. The accompanying Python code is definitely publicly offered, well documented, and comes after uniform coding requirements. The supporting data models have been cautiously selected from modern astronomical surveys (for instance, the Sloan Digital Sky Study) and so are easy to download and make use of. Together, the data models and code enable visitors to reproduce all of the figures and good examples, evaluate the strategies, and adapt them with their own areas of curiosity.

• Describes the most readily useful statistical and data-mining options for extracting understanding from huge and complicated astronomical data pieces


• Features real-world data units from modern astronomical surveys


• Runs on the freely offered Python codebase throughout


• Perfect for students and operating astronomers

See also

• Robotics, Mechatronics, and Artificial Intelligence: Experimental Circuit Blocks for Designers
• Beginning Number Theory
• Introductory Computer Science: Bits of Theory, Bytes of Practice