Selection of radio pulsar candidates using artificial neural networks

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.

Accepted for publication in Monthly Notices of the Royal Astronomical Society. 9 pages, 7 figures, and 1 table

References in corpus (3)

• Discovery of 28 pulsars using new techniques for sorting pulsar candidates
• An Interference Removal Technique for Radio Pulsar Searches
• Morphology Classification and Photometric Redshift Measurement of Galaxies

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