Abstract
This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.
Highlights
IntroductionToday’s astrophysicists are frequently dealing with the analysis of complex data from one or more astronomical surveys, which typically contain millions or even hundreds of millions of sources, from which they want to determine attributes such as their membership to a given class of astronomical objects (star, galaxy, and quasar), or their main physical parameters
Today’s astrophysicists are frequently dealing with the analysis of complex data from one or more astronomical surveys, which typically contain millions or even hundreds of millions of sources, from which they want to determine attributes such as their membership to a given class of astronomical objects, or their main physical parameters
We present the application of a variety of Artificial Intelligence (AI)-based techniques to the retrieval of information present in stellar spectra obtained from telescopes, with the goal of providing a reliable hybrid system that makes it easier for the astronomer to classify stars in the MK system, a standard in stellar astrophysics
Summary
Today’s astrophysicists are frequently dealing with the analysis of complex data from one or more astronomical surveys, which typically contain millions or even hundreds of millions of sources, from which they want to determine attributes such as their membership to a given class of astronomical objects (star, galaxy, and quasar), or their main physical parameters. To analyze the information in these enormous volumes of data, it is necessary to resort to automatic processing techniques. Many of these files are open to the international scientific community for study, but their analysis is a challenge for astronomers of the 21st century, since it requires mastery of advanced computing techniques, based on statistics and the use of methodologies such as those derived from Artificial Intelligence (AI), in what has come to be called “data mining in astronomy”. The relative intensities of these spectral characteristics are strongly dependent on the physical features (temperature, pressure, etc.) and on the presence and quantities of chemical elements in the stellar atmosphere, in such a way that stellar spectroscopy has become one of the most important tools to study those properties in stars
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