STELLAR SPECTRAL CLASSIFICATION USING AUTOMATED SCHEMES

Classification of stellar spectra by human experts, in the past, has b een subjective, leading to many nonunique databases. However, with the availability of large spectral databases, automated classification sc hemes offer an alternative to visual classification. Here, we present two schemes for automated classification of stellar spectra, namely, c hi2-minimization and Artificial Neural Network. These techniques have been applied to classify a complete set of 158 test spectra into 55 sp ectral types of a reference library. Using these methods, we have succ essfully classified the test library on the basis of reference library to an accuracy of two spectral subclasses. Such automated schemes wou ld in the future provide fast, uniform, and almost on-line classificat ion of stellar spectra.