Wspc Handbook Of Astronomical Instrumentation, The (In 5 Volumes)
Wspc Handbook Of Astronomical Instrumentation, The (In 5 Volumes)

Author:

Publisher: World Scientific

Published: 2021-05-07

Total Pages: 1556

ISBN-13: 981464434X

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Review of Volume 4:'The Handbook can be a good reference for a higher-degree science student approaching the subject or for an expert in a similar field in astronomical instrumentation. The reader requiring an in-depth presentation of a specific topic will be guided by the rich reference lists included at the end of each chapter.'The ObservatoryOur goal is to produce a comprehensive handbook of the current state of the art of astronomical instrumentation with a forward view encompassing the next decade. The target audience is graduate students with an interest in astronomical instrumentation, as well as practitioners interested in learning about the state of the art in another wavelength band or field closely related to the one in which they currently work. We assume a working knowledge of the fundamental theory: optics, semiconductor physics, etc. The purpose of this handbook is to bring together some of the leading experts in the world to discuss the frontier of astronomical instrumentation across the electromagnetic spectrum and extending into multimessenger astronomy.

Statistics, Data Mining, and Machine Learning in Astronomy
Statistics, Data Mining, and Machine Learning in Astronomy

Author: Željko Ivezić

Publisher: Princeton University Press

Published: 2020

Total Pages: 548

ISBN-13: 0691198306

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"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. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. 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"--

Astronomical Image and Data Analysis
Astronomical Image and Data Analysis

Author: J.-L. Starck

Publisher: Springer Science & Business Media

Published: 2007-06-21

Total Pages: 338

ISBN-13: 3540330259

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With information and scale as central themes, this comprehensive survey explains how to handle real problems in astronomical data analysis using a modern arsenal of powerful techniques. It treats those innovative methods of image, signal, and data processing that are proving to be both effective and widely relevant. The authors are leaders in this rapidly developing field and draw upon decades of experience. They have been playing leading roles in international projects such as the Virtual Observatory and the Grid. The book addresses not only students and professional astronomers and astrophysicists, but also serious amateur astronomers and specialists in earth observation, medical imaging, and data mining. The coverage includes chapters or appendices on: detection and filtering; image compression; multichannel, multiscale, and catalog data analytical methods; wavelets transforms, Picard iteration, and software tools. This second edition of Starck and Murtagh's highly appreciated reference again deals with topics that are at or beyond the state of the art. It presents material which is more algorithmically oriented than most alternatives and broaches new areas like ridgelet and curvelet transforms. Throughout the book various additions and updates have been made.