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Automated Detection of Classical Novae with Neural Networks
The POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalogue is also a prime source of lightcurves of variable and transient objects, including classical novae (CNe). A reliable means of identification, combined with a thorough...
Autores principales: | , , , , , , , , , , , |
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Lenguaje: | eng |
Publicado: |
2005
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1086/430844 http://cds.cern.ch/record/832142 |
_version_ | 1780905829911756800 |
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author | Feeney, S. Belokurov, V. Evans, N.W. An, J. Hewett, Paul C. Bode, M. Darnley, M. Kerins, E. Baillon, P. Carr, Bernard J. Paulin-Henriksson, S. Gould, A. |
author_facet | Feeney, S. Belokurov, V. Evans, N.W. An, J. Hewett, Paul C. Bode, M. Darnley, M. Kerins, E. Baillon, P. Carr, Bernard J. Paulin-Henriksson, S. Gould, A. |
author_sort | Feeney, S. |
collection | CERN |
description | The POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalogue is also a prime source of lightcurves of variable and transient objects, including classical novae (CNe). A reliable means of identification, combined with a thorough survey of the variable objects in M31, provides an excellent opportunity to locate and study an entire galactic population of CNe. This paper presents a set of 440 neural networks, working in 44 committees, designed specifically to identify fast CNe. The networks are developed using training sets consisting of simulated novae and POINT-AGAPE lightcurves, in a novel variation on K-fold cross-validation. They use the binned, normalised power spectra of the lightcurves as input units. The networks successfully identify 9 of the 13 previously identified M31 CNe within their optimal working range (and 11 out of 13 if the network error bars are taken into account). They provide a catalogue of 19 new candidate fast CNe, of which 4 are strongly favoured. |
id | cern-832142 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2005 |
record_format | invenio |
spelling | cern-8321422023-03-14T19:53:44Zdoi:10.1086/430844http://cds.cern.ch/record/832142engFeeney, S.Belokurov, V.Evans, N.W.An, J.Hewett, Paul C.Bode, M.Darnley, M.Kerins, E.Baillon, P.Carr, Bernard J.Paulin-Henriksson, S.Gould, A.Automated Detection of Classical Novae with Neural NetworksAstrophysics and AstronomyThe POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalogue is also a prime source of lightcurves of variable and transient objects, including classical novae (CNe). A reliable means of identification, combined with a thorough survey of the variable objects in M31, provides an excellent opportunity to locate and study an entire galactic population of CNe. This paper presents a set of 440 neural networks, working in 44 committees, designed specifically to identify fast CNe. The networks are developed using training sets consisting of simulated novae and POINT-AGAPE lightcurves, in a novel variation on K-fold cross-validation. They use the binned, normalised power spectra of the lightcurves as input units. The networks successfully identify 9 of the 13 previously identified M31 CNe within their optimal working range (and 11 out of 13 if the network error bars are taken into account). They provide a catalogue of 19 new candidate fast CNe, of which 4 are strongly favoured.The POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalogue is also a prime source of lightcurves of variable and transient objects, including classical novae (CNe). A reliable means of identification, combined with a thorough survey of the variable objects in M31, provides an excellent opportunity to locate and study an entire galactic population of CNe. This paper presents a set of 440 neural networks, working in 44 committees, designed specifically to identify fast CNe. The networks are developed using training sets consisting of simulated novae and POINT-AGAPE lightcurves, in a novel variation on K-fold cross-validation. They use the binned, normalised power spectra of the lightcurves as input units. The networks successfully identify 9 of the 13 previously identified M31 CNe within their optimal working range (and 11 out of 13 if the network error bars are taken into account). They provide a catalogue of 19 new candidate fast CNe, of which 4 are strongly favoured.astro-ph/0504236oai:cds.cern.ch:8321422005-04-11 |
spellingShingle | Astrophysics and Astronomy Feeney, S. Belokurov, V. Evans, N.W. An, J. Hewett, Paul C. Bode, M. Darnley, M. Kerins, E. Baillon, P. Carr, Bernard J. Paulin-Henriksson, S. Gould, A. Automated Detection of Classical Novae with Neural Networks |
title | Automated Detection of Classical Novae with Neural Networks |
title_full | Automated Detection of Classical Novae with Neural Networks |
title_fullStr | Automated Detection of Classical Novae with Neural Networks |
title_full_unstemmed | Automated Detection of Classical Novae with Neural Networks |
title_short | Automated Detection of Classical Novae with Neural Networks |
title_sort | automated detection of classical novae with neural networks |
topic | Astrophysics and Astronomy |
url | https://dx.doi.org/10.1086/430844 http://cds.cern.ch/record/832142 |
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