Automated spectral classification of QSOs and galaxies by radial basis function network with dynamic decay adjustment

Abstract

This paper presents a fast neural network method of radial basis function with dynamic decay adjustment (RBFN-DDA) to classify Quasi-Stellar Objects (QSOs) and galaxies automatically. The classification process is mainly comprised of three parts: (1) the dimensions of the normalized input spectra is reduced by the Principal Component Analysis (PCA); (2) the network is built from scratch: the number of required hidden units is determined during training and the individual radii of the Gaussians are adjusted dynamically until corresponding criterions are satisfied; (3) The trained network is used for the classification of the real spectra of QSOs and galaxies. The method of RBFN-DDA having constructive and fast training process solves the difficulty of selecting appropriate number of neurons before training in many methods of neural networks and achieves lower error rates of spectral classification. Besides, due to its efficiency, the proposed method would be particularly useful for the fast and automatic processing of voluminous spectra to be produced from the large-scale sky survey project. © Springer-Verlag Berlin Heidelberg 2006.