Automated Detection of Classical Novae with Neural Networks
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.
Cite
@article{arxiv.astro-ph/0504236,
title = {Automated Detection of Classical Novae with Neural Networks},
author = {S. Feeney and V. Belokurov and N. W. Evans and J. An and P. C. Hewett and M. Bode and M. Darnley and E. Kerins and P. Baillon and B. J. Carr and S. Paulin-Henriksson and A. Gould},
journal= {arXiv preprint arXiv:astro-ph/0504236},
year = {2008}
}
Comments
28 pages, 8 figures, The Astronomical Journal (in press)