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| SATS: A Stellar Spectral Classification Algorithm Based on Multiple Feature Extraction |
| TU Liang-ping1, 2, LI Shuang-chuan2, TU Dong-xin1*, LI Jian-xi*, DING Zhi-chao2 |
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, China
2. School of Science，University of Science and Technology Liaoning，Anshan 114051，China
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Abstract An in-depth study of a stellar's spectrum provides insight into its chemical composition and physical properties. Stellar spectrum classification is an important direction in stellar spectrum research. With the emergence of massive stellar spectrum data, artificial classification cannot meet scientific research needs. Based on this, this paper constructs the SATS algorithm, which realizes the automatic classification of F, G, and K-type stellar spectra. Firstly, the SATS algorithm uses singular value decomposition (SVD) to denoise the normalized stellar spectra. Then, the SATS algorithm performs feature extraction on the stellar spectrum. The feature extraction layer consists of six modules:Incremental principal component analysis (IPCA), nuclear principal component analysis (KPCA), sparse principal component analysis (SparsePCA), FactorAnalysis, independent component analysis (FastICA) and Transformer(the six modules are collectively referred to as Analysis module), to ensure that the variance contribution rate is above 0.95, IPCA, KPCA, SparsePCA, FactorAnalysis and FastICA extract the stellar spectral features into 300 dimensions. Finally, the stellar spectra are fed into the SoftMax layer for automatic classification. SATS algorithm combines multiple analysis modules to improve the accuracy of classification further using a single analysis module. Once again, the combination of Transformer modules and multiple Analysis modules improves classification accuracy. The most significant advantage of the SATS algorithm is that it performs multiple feature extraction on the stellar spectrum, which retains the stellar spectral information to the maximum extent and minimizes the information loss by different feature extraction methods. The final classification accuracy of the SATS algorithm is 0.93, which classification accuracy is also higher than that of the hybrid deep learning algorithms CNN+Bayes, CNN+Knn, CNN+SVM, CNN+Adaboost and CNN+Adaboost 0.86, 0.88, 0.89, 0.87, 0.89.
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Received: 2022-11-22
Accepted: 2023-10-02
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Corresponding Authors:
TU Dong-xin, LI Jian-xi
E-mail: ptjxli@hotmail.com
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