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Author: Linghe Kong Publisher: Elsevier ISBN: 012819085X Category : Science Languages : en Pages : 440
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
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: Kieran Jay Edwards Publisher: Springer Science & Business Media ISBN: 3319065998 Category : Technology & Engineering Languages : en Pages : 112
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: Michael J. Way Publisher: CRC Press ISBN: 1439841748 Category : Computers Languages : en Pages : 744
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
Author: Željko Ivezić Publisher: Princeton University Press ISBN: 0691151687 Category : Science Languages : en Pages : 550
Book Description
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. 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. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers
Author: M.A Albrecht Publisher: Springer Science & Business Media ISBN: 940113250X Category : Science Languages : en Pages : 284
Book Description
Data archiving has, for many years, been the most disregarded aspect of all data systems. The increase in numbers of telescopes, both groundbased and space-borne, and the increase in efficiency of detectors have generated overwhelming amounts of data. Much of these data were and are not used on short timescales and (should) have been archived, where they can be used later and/or by others. Archiving is essential. Objects can change in the course of time. New technological or scientific developments might require observing objects again. The cost-benefit ratio will become more and more important when considering the allocation of telescope time. The retrieval of `old' data can then be crucial. At present there are a number of data collections and data retrieval systems. This book includes a series of clear and up-to-date descriptions of many important available data systems. For professional astronomers, librarians and computer engineers.
Author: Phyllis S. Glaeser Publisher: CRC Press ISBN: 9780891169314 Category : Art Languages : en Pages : 408
Book Description
Topics dealt with: Bioscience and biotechnology; Industry and technology; Safety and environmental protection; Geo- and space sciences; Scientific aspects of collecting and distributing data; Legal and social aspects of data dissemination; Innovations in data handling.
Author: D Egret Publisher: Springer Science & Business Media ISBN: 9401103976 Category : Mathematics Languages : en Pages : 291
Book Description
The ability of storing, managing, and giving access to the huge quantity of data collected by astronomical observatories is one of the major challenges of modern astronomy. At the same time, the growing complexity of data systems implies a change of concepts: the scientist has to manipulate data as well as information. Recent developments of the `WorldWideWeb' bring interesting answers to these problems. The book presents a wide selection of databases, archives, data centers, and information systems. Clear and up-to-date descriptions are included, together with their scientific context and motivations. Audience: This volume provides an essential tool for astronomers, librarians, data specialists and computer engineers.
Author: Jianhui Li Publisher: Springer ISBN: 3030280616 Category : Computers Languages : en Pages : 346
Book Description
This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018. The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies.
Author: Andre Heck Publisher: Springer Science & Business Media ISBN: 0585331103 Category : Science Languages : en Pages : 213
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: Linghe Kong
Author: Linghe Kong
Author: Kieran Jay Edwards
Author: Michael J. Way
Author: Željko Ivezić
Author: M.A Albrecht
Author: Patrick L. Shopbell
Author: Phyllis S. Glaeser
Author: D Egret
Author: Jianhui Li
Author: Andre Heck