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E. F. Vieira
Laboratorio de Astrofísica Espacial y Física Fundamental
P.O.Box 50727, 28080 Madrid, Spain, Email: efv@laeff.esa.es
J. D. Ponz
GSED/ESA, Villafranca
P.O.Box 50727, 28080 Madrid, Spain, Email: jdp@vilspa.esa.es
The present work has been done within the context of the IUE Final Archive project, to provide an efficient and objective classification procedure to analyze the complete IUE database, based on methods that do not require prior knowledge about the object to be classified. Two methods are compared: a supervised ANN classifier and an unsupervised Self Organized Map (SOM) classifier.
The actual input set was obtained by merging together data from the two IUE
cameras, sampled at a uniform wavelength step of 2 Å, after
processing with the standard calibration pipeline. Although the
spectra are good in quality, there are two aspects that seriously
hinder the automated classification: interstellar extinction and
contamination with geo-coronal Ly- emission. Some
pre-processing was required to eliminate these effects and to
normalize the data.
All spectra were corrected for interstellar extinction by using
Seaton's (1979) extinction law. Figure 1 shows original and corrected
spectra, corresponding to a O4 star; the wavelength range used in the
classification is indicated by the solid line.
A supervised classification scheme based on artificial neural networks (ANN) has been used. This technique was originally developed by McCullogh and Pitts (1943) and has been generalized with an algorithm for training networks having multiple layers, known as back-propagation (Rumelhart et al. 1986).
The complete sample in the Atlas was divided into two sets: 64 standard stars, with spectral types from O3 to G5, was used as the training set. The remaining spectra were used as a test to exercise the classification algorithm. The network contains 744 × 120 × 120 × 51 neurons. The resulting classification error on the test set was 1.1 spectral subclasses. Figure 2 shows the classification diagrams, comparing automatic classification (ANN) with manual (Atlas) and with a simple metric distance algorithm.
In the Self Organized Map (SOM) the net organizes the spectra into clusters based on similarities using a metric to define the distance between two spectra. The algorithm used to perform such clustering was developed by Kohonen (1984).
A 8 × 8 map with 744 neurons in the input layer was exercised on the same input sample. The training set was used to define the spectral types associated to the elements in the map. This classifier gives an error of 1.62 subclasses when compared with the Atlas, with a correlation of 0.9844. In addition, 27 stars could not be classified according to the classification criterion used in this experiment. Figure 3 shows the classification diagrams, comparing the SOM classifier with ANN and manual classification.
These methods can be directly applied to the set of spectra, without previous analysis of spectral features.
Heck, A., Egret, D., Jaschek, M., & Jaschek, C. 1983, (Feb), IUE Low Dispersion Spectra Reference Atlas, SP 1052, ESA, Part1. Normal Stars
Jaschek, M., & Jaschek, C. 1984, in The MK process and Stellar Classification, David Dunlap Observatory, 290
Kohonen, T. 1984, Self-organization and Associative
Memory Volume 8 of
Springer Series in Information Sciences (Springer
Verlag, Nueva York)
McCullogh, W.S., & Pitts, W.H. 1943, Bull. Math. Biophysics, 5, 115
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. 1986, Nature, 323, 533
Seaton, M.J. 1979, MNRAS, 187, 73P
Vieira, E.F., Ponz, J.D. 1995, A&AS, 111, 393
Wamsteker, W., Driessen, C., & Muñoz, J.R. et al. 1989, A&AS, 79, 1
Next: Methods for Structuring and Searching Very Large Catalogs
Up:
Astrostatistics and Databases
Previous: An Optimal Data Loss Compression Technique for Remote
Surface Multiwavelength Mapping
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