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Author: Linghe Kong
Publisher: Elsevier
ISBN: 012819085X
Category : Science
Languages : en
Pages : 440
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Book Description
Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world's largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. - Bridges the gap between radio astronomy and computer science - Includes coverage of the observation lifecycle as well as data collection, processing and analysis - Presents state-of-the-art research and techniques in big data related to radio astronomy - Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)
Author: Linghe Kong
Publisher: Elsevier
ISBN: 012819085X
Category : Science
Languages : en
Pages : 440
Get Book Here
Book Description
Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world's largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. - Bridges the gap between radio astronomy and computer science - Includes coverage of the observation lifecycle as well as data collection, processing and analysis - Presents state-of-the-art research and techniques in big data related to radio astronomy - Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)
Author: European Southern Observatory
Publisher:
ISBN:
Category : Astronomy
Languages : en
Pages : 536
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Publisher:
ISBN:
Category :
Languages : un
Pages :
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Book Description
Author: Kieran Jay Edwards
Publisher: Springer Science & Business Media
ISBN: 3319065998
Category : Technology & Engineering
Languages : en
Pages : 112
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Book Description
With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
Author: Petr Skoda
Publisher: Elsevier
ISBN: 0128191554
Category : Computers
Languages : en
Pages : 474
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Book Description
Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. - Addresses both astronomy and geosciences in parallel, from a big data perspective - Includes introductory information, key principles, applications and the latest techniques - Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
Author: André Heck
Publisher:
ISBN:
Category : Astronomy
Languages : en
Pages : 554
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Book Description
Author: Luis Manuel Sarro
Publisher: Springer Science & Business Media
ISBN: 1461433231
Category : Science
Languages : en
Pages : 259
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Book Description
This volume provides an overview of the field of Astrostatistics understood as the sub-discipline dedicated to the statistical analysis of astronomical data. It presents examples of the application of the various methodologies now available to current open issues in astronomical research. The technical aspects related to the scientific analysis of the upcoming petabyte-scale databases are emphasized given the importance that scalable Knowledge Discovery techniques will have for the full exploitation of these databases. Based on the 2011 Astrostatistics and Data Mining in Large Astronomical Databases conference and school, this volume gathers examples of the work by leading authors in the areas of Astrophysics and Statistics, including a significant contribution from the various teams that prepared for the processing and analysis of the Gaia data.
Author: Andre Heck
Publisher: Springer Science & Business Media
ISBN: 0585331103
Category : Science
Languages : en
Pages : 213
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Book Description
Intelligent information Retrieval comprehensively surveys scientific information retrieval, which is characterized by growing convergence of information expressed in varying complementary forms of data - textual, numerical, image, and graphics; by the fundamental transformation which the scientific library is currently being subjected to; and by computer networking which as become an essential element of the research fabric. Intelligent Information Retrieval addresses enabling technologies, so-called `wide area network resource discovery tools', and the state of the art in astronomy and other sciences. This work is essential reading for astronomers, scientists in related disciplines, and all those involved in information storage and retrieval.
Author: Michael J. Way
Publisher: CRC Press
ISBN: 1439841748
Category : Computers
Languages : en
Pages : 744
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Book Description
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines
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ISBN:
Category :
Languages : de
Pages :
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