Rapid data classification via Kohonen self-organising maps
I here present my version of the Kohonen Self-Organising Map (KSOM) as applied to the classification, or rather clustering, of astronomical data. The main body of this work is concerned with the grouping of period-folded stellar lightcurves and clustering based on the lightcurve shape alone. It has been found that the algorithm is an extremely stable grouping mechanism for data of low (3cr signal to noise) to good quality. With further analysis of the results it is possible to locate underpopulated samples of data that exist within the data. This can be successfully achieved for samples of 1%, or less, total population. Additionally the same algorithm has been applied to the extraction of planetary transit lightcurves from those of eclipsing binaries (chapter 5), and to the grouping of X-ray/optical data from the XMM-Subaru deep-field observations (chapter 6). In both cases the algorithm has shown itself to be quite capable of performing such tasks and as such I propose that it could become a very useful astronomical tool. In summary I also present a few ideas for further refinement of the results presented by the KSOM and how these may be used in future study.