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Machine Learning for Pulsar Detection

Published online by Cambridge University Press:  04 June 2018

Rebecca McFadden ,
Aris Karastergiou  and
Stephen Roberts
Rebecca McFadden
Affiliation:
Department of Engineering Science, University of Oxford, Oxford, UK email: rebecca.mcfadden@eng.ox.ac.uk
Aris Karastergiou
Affiliation:
Department of Physics, University of Oxford, Oxford, UK
Stephen Roberts
Affiliation:
Department of Engineering Science, University of Oxford, Oxford, UK email: rebecca.mcfadden@eng.ox.ac.uk
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Abstract

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The next generation of radio telescopes will have unprecedented sensitivity and time-resolution offering exciting new capabilities in time-domain science. However, this will result in very large numbers of pulsar and transient event candidates and the associated data rates will be technically challenging in terms of data storage and signal processing. Automated detection and classification techniques are therefore required and must be optimized to allow high-throughput data processing in real time. In this paper we provide a summary of the emerging machine learning techniques being applied to this problem.

Keywords

pulsars: generalmethods: data analysismethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S337: Pulsar Astrophysics the Next Fifty Years , September 2017 , pp. 372 - 373
Copyright
Copyright © International Astronomical Union 2018 

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