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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
In this paper we have analyzed IUE high resolution spectra of the central star (BD+602522) of the Bubble nebula. We discuss velocities of the different regions along the line of sight to the bubble. We find that the Bubble Nebula is younger (by a fac
We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R $sim100$) data. The goal of SNIascore is fully automated classification of SNe Ia with a very low
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We present an automated classification of stars exhibiting periodic, non-periodic and irregular light variations. The Hipparcos catalogue of unsolved variables is employed to complement the training set of periodic variables of Dubath et al. with irr
We present a novel automated methodology to detect and classify periodic variable stars in a large database of photometric time series. The methods are based on multivariate Bayesian statistics and use a multi-stage approach. We applied our method to