Selection of radio pulsar candidates using artificial neural networks

R. P. Eatough; N. Molkenthin; M. Kramer; A. Noutsos; M. J. Keith; B. W. Stappers; A. G. Lyne

doi:10.1111/j.1365-2966.2010.17082.x

Selection of radio pulsar candidates using artificial neural networks

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

Research output: Contribution to journal › Article › peer-review

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. © 2010 The Authors. Journal compilation. © 2010 RAS.

Original language	English
Pages (from-to)	2443-2450
Number of pages	7
Journal	Monthly Notices of the Royal Astronomical Society
Volume	407
Issue number	4
DOIs	https://doi.org/10.1111/j.1365-2966.2010.17082.x
Publication status	Published - Oct 2010

Keywords

Methods: data analysis
Pulsars: general
Stars: neutron

Access to Document

10.1111/j.1365-2966.2010.17082.x

http://adsabs.harvard.edu/abs/2010MNRAS.407.2443E

Cite this

@article{e089e9c6145b42f6909c7b93b3bd5f14,

title = "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. {\textcopyright} 2010 The Authors. Journal compilation. {\textcopyright} 2010 RAS.",

keywords = "Methods: data analysis, Pulsars: general, Stars: neutron",

author = "Eatough, {R. P.} and N. Molkenthin and M. Kramer and A. Noutsos and Keith, {M. J.} and Stappers, {B. W.} and Lyne, {A. G.}",

year = "2010",

month = oct,

doi = "10.1111/j.1365-2966.2010.17082.x",

language = "English",

volume = "407",

pages = "2443--2450",

journal = "Monthly Notices of the Royal Astronomical Society",

issn = "1365-2966",

publisher = "Oxford University Press",

number = "4",

}

TY - JOUR

T1 - Selection of radio pulsar candidates using artificial neural networks

AU - Eatough, R. P.

AU - Molkenthin, N.

AU - Kramer, M.

AU - Noutsos, A.

AU - Keith, M. J.

AU - Stappers, B. W.

AU - Lyne, A. G.

PY - 2010/10

Y1 - 2010/10

N2 - 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. © 2010 The Authors. Journal compilation. © 2010 RAS.

AB - 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. © 2010 The Authors. Journal compilation. © 2010 RAS.

KW - Methods: data analysis

KW - Pulsars: general

KW - Stars: neutron

U2 - 10.1111/j.1365-2966.2010.17082.x

DO - 10.1111/j.1365-2966.2010.17082.x

M3 - Article

SN - 1365-2966

VL - 407

SP - 2443

EP - 2450

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 4

ER -

Selection of radio pulsar candidates using artificial neural networks

Abstract

Keywords

Access to Document

Fingerprint

Cite this