Overview
- Presents recent applications of Big Data research to Astronomy
- Demonstrates the application of Big data to the Galaxy Zoo project, where a large collection of galaxy images are annotated by citizen scientists
- Presents a Data Clustering Approach to Identifying Uncertain Galaxy Morphology
Part of the book series: Studies in Big Data (SBD, volume 6)
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Table of contents (8 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Astronomy and Big Data
Book Subtitle: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology
Authors: Kieran Jay Edwards, Mohamed Medhat Gaber
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-06599-1
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-06598-4Published: 29 April 2014
Softcover ISBN: 978-3-319-38328-6Published: 03 September 2016
eBook ISBN: 978-3-319-06599-1Published: 12 April 2014
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XII, 105
Number of Illustrations: 30 b/w illustrations, 24 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Astronomy, Observations and Techniques, Data Mining and Knowledge Discovery