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[Spectral classification based on Bayes decision]
Rong Liu1, Hong-Mei Jin, Fu-Qing Duan
1Base Department, Beijing Institute of Clothing Technology, Beijing 100029, China.
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|May 26, 2010
Summary
Automated spectral analysis for astronomical surveys uses Bayes decision theory. Optimal kernel width in Parzen window estimation is crucial for accurate classification of stars, galaxies, and quasars.
Area of Science:
- Astronomy and Astrophysics
- Computer Science
Context:
- Large astronomical sky surveys generate massive datasets of celestial spectra.
- Automated analysis is essential for efficient processing of this data.
Purpose:
- To develop and evaluate an automated spectral classification method.
- To classify celestial spectra into star, galaxy, and quasar types using Bayes decision theory.
Summary:
- Employs Principal Component Analysis (PCA) for feature extraction, projecting spectra into a 3D PCA space.
- Utilizes Parzen window estimation for class conditional probability densities.
- Applies a minimum error Bayes decision rule for classification, analyzing kernel width's impact on accuracy.
Impact:
- Provides an effective automated method for spectral classification.
- Identifies the critical role of kernel width in Parzen window density estimation for classification accuracy.
- Contributes to efficient analysis of large astronomical spectral datasets.
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