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Machine-learning in astronomy

Published online by Cambridge University Press:  01 July 2015

Michael Hobson ,
Philip Graff ,
Farhan Feroz  and
Anthony Lasenby
Michael Hobson
Affiliation:
Astrophysics Group, Cavendish Laboratory, J.J. Thomson Avenue, Cambridge, CB3 0HE, UK email: mph@mrao.cam.ac.uk, ff235@mrao.cam.ac.uk, anthony@mrao.cam.ac.uk
Philip Graff
Affiliation:
Gravitational Astrophysics Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771, USA email: philip.b.graff@nasa.gov
Farhan Feroz
Affiliation:
Astrophysics Group, Cavendish Laboratory, J.J. Thomson Avenue, Cambridge, CB3 0HE, UK email: mph@mrao.cam.ac.uk, ff235@mrao.cam.ac.uk, anthony@mrao.cam.ac.uk
Anthony Lasenby
Affiliation:
Astrophysics Group, Cavendish Laboratory, J.J. Thomson Avenue, Cambridge, CB3 0HE, UK email: mph@mrao.cam.ac.uk, ff235@mrao.cam.ac.uk, anthony@mrao.cam.ac.uk
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Abstract

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Machine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, called SkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. The SkyNet and BAMBI packages, which are fully parallelised using MPI, are available at http://www.mrao.cam.ac.uk/software/.

Keywords

methods: data analysismethods: statisticalcosmological parametersgamma rays: bursts

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