The automated classification of astronomical lightcurves using Kohonen self-organising maps

Abstract

We apply the technique of self-organising maps (Kohonen 1990) to the automated classification of singly periodic astronomical lightcurves. We find that our maps readily distinguish between lightcurve types in both synthetic and real datasets, and that the resulting maps do not depend sensitively on the chosen learning parameters. Automated data analysis techniques are likely to be become increasingly important as the size of astronomical datasets continues to increase, particularly with the advent of ultra-wide-field survey telescopes such as WASP, RAPTOR and ASAS.

Accepted for publication in MNRAS

References in corpus (4)

• Northern Sky Variability Survey (NSVS): Public data release
• ROTSE All Sky Surveys for Variable Stars I: Test Fields
• The RAPTOR Experiment: A System for Monitoring the Optical Sky in Real Time
• Automated Source Classification using a Kohonen Network

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