Vol 5, No 2

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks

Ling Bai, Ping Guo, Zhan-Yi Hu


Abstract An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification. Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.


Keywords methods: data analysis --- techniques: spectroscopic --- stars: general --- galaxies: stellar content

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