Abstract

Artificial Neural Network (ANN) is a versatile tool which has been used both in academic research and industrial applications. In astronomy, this technique has been used for a variety of applications, such as telescope adaptive optics, classifying galaxies, and separating stars from galaxies. The classification of a large database of stellar spectra, which would be a Herculean task for human classifiers if done visually, is an ideal problem for the ANN technique, which can handle such problems without manual intervention. Recently, increased computational power, combined with improvement in the ANN techniques, has provided an efficient way to perform automatic classification. We have implemented ANN to classify stellar spectra from large spectral databases. We present here the Multilayer Back Propagation Network (MBPN), which is used to classify stellar spectra obtained in the optical and ultraviolet regions. The performance of MBPN shows that the ANN is capable of classifying ultraviolet stellar spectra to an accuracy of about one spectral subclass for most of the cases. The scope of this technique is expected to be expanded with the availability of large homogeneous digitized stellar spectral databases.