Automated Detection of Classical Novae with Neural Networks

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dc.creatorFeeney, S.
dc.creatorBelokurov, V.
dc.creatorEvans, N. W.
dc.creatorAn, J.
dc.creatorHewett, P. C.
dc.creatorBode, M.
dc.creatorDarnley, M.
dc.creatorKerins, E.
dc.creatorBaillon, P.
dc.creatorCarr, B. J.
dc.creatorPaulin-Henriksson, S.
dc.creatorGould, A.
dc.date2005-04-11
dc.date.accessioned2026-07-07T11:29:41Z
dc.date.available2026-07-07T11:29:41Z
dc.descriptionThe 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.
dc.description28 pages, 8 figures, The Astronomical Journal (in press)
dc.identifierhttps://arxiv.org/abs/astro-ph/0504236
dc.identifierhttp://arxiv.org/abs/astro-ph/0504236
dc.identifierAstron.J.130:84-94,2005
dc.identifierdoi:10.1086/430844
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/196793
dc.subjectAstrophysics
dc.titleAutomated Detection of Classical Novae with Neural Networks
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