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.
@article{arxiv.1005.5068,
title = {Selection of radio pulsar candidates using artificial neural networks},
author = {R. P. Eatough and N. Molkenthin and M. Kramer and A. Noutsos and M. J. Keith and B. W. Stappers and A. G. Lyne},
journal= {arXiv preprint arXiv:1005.5068},
year = {2015}
}
Comments
Accepted for publication in Monthly Notices of the Royal Astronomical Society. 9 pages, 7 figures, and 1 table