Hostname: page-component-5c6d5d7d68-tdptf Total loading time: 0 Render date: 2024-08-18T06:02:30.447Z Has data issue: false hasContentIssue false

Classification of galaxy type from images using Microsoft R Server

Published online by Cambridge University Press:  30 May 2017

Andrie de Vries*
Affiliation:
Microsoft Microsoft UK Ltd, Algorithms and Data Science, 2 Kingdom Street, Paddington, London, UKW2 6BD email: adevries@microsoft.com
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Many astronomers working in the field of AstroInformatics write code as part of their work. Although the programming language of choice is Python, a small number (8%) use R. R has its specific strengths in the domain of statistics, and is often viewed as limited in the size of data it can handle. However, Microsoft R Server is a product that removes these limitations by being able to process much larger amounts of data. I present some highlights of R Server, by illustrating how to fit a convolutional neural network using R. The specific task is to classify galaxies, using only images extracted from the Sloan Digital Skyserver.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2017 

References

Galaxyzoo web site, https://www.galaxyzoo.org/. Accessed: 2016-11-29.Google Scholar
Nasa, Hubble reveals observable universe contains 10 times more galaxies than previously thought. https://www.nasa.gov/feature/goddard/2016/hubble-reveals-observable-universe-contains-10-times-more-galaxies-than-previously-thought Accessed: 2016-11-29.Google Scholar
Microsoft, Microsoft r server website https://msdn.microsoft.com/en-us/microsoft-r/. Accessed: 2016-11-29.Google Scholar
Microsoft, R server getting started. https://msdn.microsoft.com/en-us/microsoft-r/microsoft-r-getting-started. Accessed: 2016-11-29.Google Scholar
Lee, Honglak, Grosse, Roger, Ranganath, Rajesh, and Ng, Andrew Y., Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, pages 609–616, New York, NY, USA, 2009. ACM.Google Scholar
R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. ISBN 3-900051-07-0.Google Scholar