Automated classification of IUE low dispersion spectra (I)

TL;DR

This paper compares metric distance and neural network methods for automated classification of IUE low-dispersion spectra, finding neural networks more accurate in classifying stars from O3 to G5 with high precision.

Contribution

It introduces and evaluates neural networks for spectral classification, demonstrating improved accuracy over traditional metric distance methods.

Findings

Neural networks outperform metric distance in classification accuracy.

Achieved spectral classification accuracy within 1.1 subclasses.

Applicable to large spectral archives for objective analysis.

Abstract

Along the life of the IUE project, a large archive with spectral data has been generated, requiring automated classification methods to be analyzed in an objective form. Previous automated classification methods used with IUE spectra were based on multivariate statistics. In this paper, we compare two classification methods that can be directly applied to spectra in the archive: metric distance and artificial neural networks. These methods are used to classify IUE low-dispersion spectra of normal stars with spectral types ranging from O3 to G5. The classification based on artificial neural networks performs better than the metric distance, allowing the determination of the spectral classes with an accuracy of 1.1 spectral subclasses. KeyWords: data analysis, spectroscopic, fundamental parameters