Automated Detection of Classical Novae with Neural Networks

S. Feeney; V. Belokurov; N. W. Evans; J. An; P. C. Hewett; M. Bode; M. Darnley; E. Kerins; P. Baillon; B. J. Carr; S. Paulin-Henriksson; A. Gould

doi:10.1086/430844

arXiv · astro-ph/0504236

Automated Detection of Classical Novae with Neural Networks

S. Feeney·V. Belokurov·N. W. Evans·J. An·P. C. Hewett·M. Bode·M. Darnley·E. Kerins·P. Baillon·B. J. Carr·S. Paulin-Henriksson·A. Gould

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Abstract

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.

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S. Feeney, V. Belokurov, N. W. Evans, J. An, P. C. Hewett, M. Bode, M. Darnley, E. Kerins, P. Baillon, B. J. Carr, S. Paulin-Henriksson, A. Gould. 2005-04-11. Automated Detection of Classical Novae with Neural Networks. https://doi.org/10.1086/430844

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