图书名称: Astronomy and Big Data: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology April 13, 2014 , Studies in Big Data Volume 6
作者:Kieran Edwards (Author), Mohamed Medhat Gaber (Author)
出版社:Springer International Publishing
页数:314
出版时间:2014
语言:English
格式:pdf
内容简介:
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From the shifting of our beloved earth away from the centre of the universe, to
discovering the billions of galaxies and stars that make up our intergalactic neigh-
bourhood,Astronomy continues to surprise and astound us. For a long time, empir-
ical results have been the only means of describing the natural phenomenon. The
age of enlightenment has witnessed the change to experimental generalisation and
modelling. With the advent of computers, computational science was the natural
progression, where simulation results can efficiently approve or discredit scientific
hypothesis.In a visionarytalk by the prominentMicrosoft researcherJim Gray, just
before he went missing in 2007, he described, in an almost poetic manner, how data
exploration has been set as the fourth paradigm in scientific research.
The research reported in this book is conducted in the realm of this fourth
paradigm. The billions of galaxies and stars that sky surveys collect every day us-
ing state-of-the-arttelescopes have overwhelmedastronomersand cosmologists.In-
evitably,computationalphysicshas fallenshort at addressingthe delugeofdata, and
new tools are needed. The term Big Data has been recently coined to describe such
large volumes of data that arrive at high velocity. An unusual idea has found its
way to prominent astronomers, that is to involve the public in some of the tasks
that require manual inspection of a huge number of images. Galaxy Zoo has been a
pioneering project that sought the help of the public in classifying galaxies into its
two main categories of morphology, spiral and elliptical, as have been classified by
Edwin Hubble some eighty years ago. This categorisation is of paramount impor-
tance to physicists, astronomers and cosmologists in their quest to find the theory of
everything.