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2010•Monthly Notices of the Royal Astronomical SocietyOpen access

Selection of radio pulsar candidates using artificial neural networks

Ralph P. Eatough, N. Molkenthin, M. Krämer, A. Noutsos, M. J. Keith, B. W. Stappers, A. G. Lyne

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Abstract

Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.

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Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.

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Available abstract

Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.

Key concepts: Pulsar, Physics, X-ray pulsar, Selection (genetic algorithm), Astronomy, Astrophysics, Millisecond pulsar, Binary pulsar

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